|
| |
| "Decision Trees, Random Forests, AdaBoost & XGBoost in Python" |
"You're looking for a complete Decision tree course that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in Python, right?You've found the right Decision Trees and tree based advanced techniques course!After completing this course you will be able to:Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost of Machine Learning.Have a clear understanding of Advanced Decision tree based algorithms such as Random Forest, Bagging, AdaBoost and XGBoostCreate a tree based (Decision tree, Random Forest, Bagging, AdaBoost and XGBoost) model in Python and analyze its result.Confidently practice, discuss and understand Machine Learning conceptsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course.If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Decision tree, Random Forest, Bagging, AdaBoost and XGBoost.Why should you choose this course?This course covers all the steps that one should take while solving a business problem through Decision tree.Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a decision tree based model, which are some of the most popular Machine Learning model, to solve business problems.Below are the course contents of this course on Linear Regression:Section 1 - Introduction to Machine LearningIn this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.Section 2 - Python basicThis section gets you started with Python.This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.Section 3 - Pre-processing and Simple Decision treesIn this section you will learn what actions you need to take to prepare it for the analysis, these steps are very important for creating a meaningful.In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split. In the end we will create and plot a simple Regression decision tree.Section 4 - Simple Classification TreeThis section we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in PythonSection 5, 6 and 7 - Ensemble techniqueIn this section we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. In this course we will discuss Random Forest, Baggind, Gradient Boosting, AdaBoost and XGBoost.By the end of this course, your confidence in creating a Decision tree model in Python will soar. You'll have a thorough understanding of how to use Decision tree modelling to create predictive models and solve business problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------------Below is a list of popular FAQs of students who want to start their Machine learning journey-What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 4 parts:Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning modelProgramming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in PythonUnderstanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.Why use Python for data Machine Learning?Understanding Python is one of the valuable skills needed for a career in Machine Learning.Though it hasnt always been, Python is the programming language of choice for data science. Heres a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggetss annual poll of data scientists most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, its nice to know that employment opportunities are abundant (and growing) as well.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledgeand further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning."
Price: 199.99
|
|
| "Decision Trees, Random Forests, Bagging & XGBoost: R Studio" |
"You're looking for a complete Decision tree course that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in R, right?You've found the right Decision Trees and tree based advanced techniques course!After completing this course you will be able to:Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost of Machine Learning.Have a clear understanding of Advanced Decision tree based algorithms such as Random Forest, Bagging, AdaBoost and XGBoostCreate a tree based (Decision tree, Random Forest, Bagging, AdaBoost and XGBoost) model in R and analyze its result.Confidently practice, discuss and understand Machine Learning conceptsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course.If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Decision tree, Random Forest, Bagging, AdaBoost and XGBoost.Why should you choose this course?This course covers all the steps that one should take while solving a business problem through Decision tree.Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a decision tree based model, which are some of the most popular Machine Learning model, to solve business problems.Below are the course contents of this course :Section 1 - Introduction to Machine LearningIn this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.Section 2 - R basicThis section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R. Section 3 - Pre-processing and Simple Decision treesIn this section you will learn what actions you need to take to prepare it for the analysis, these steps are very important for creating a meaningful.In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split. In the end we will create and plot a simple Regression decision tree.Section 4 - Simple Classification TreeThis section we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in PythonSection 5, 6 and 7 - Ensemble techniqueIn this section we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. In this course we will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.By the end of this course, your confidence in creating a Decision tree model in R will soar. You'll have a thorough understanding of how to use Decision tree modelling to create predictive models and solve business problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------------Below is a list of popular FAQs of students who want to start their Machine learning journey-What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 3 parts:Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning modelProgramming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in PythonUnderstanding of models - Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.Why use R for Machine Learning?Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R1. Its a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, its not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means its easy to find answers to questions and community guidance as you work your way through projects in R.5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier and of course, itll also make you a more flexible and marketable employee when youre looking for jobs in data science.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledgeand further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning."
Price: 29.99
|
|
| "Support Vector Machines in Python: SVM Concepts & Code" |
"You're looking for a complete Support Vector Machines course that teaches you everything you need to create a Support Vector Machines model in Python, right?You've found the right Support Vector Machines techniques course!How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course.If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Support Vector Machines.Why should you choose this course?This course covers all the steps that one should take while solving a business problem through Decision tree.Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy"
Price: 199.99
|
|
| "SVM for Beginners: Support Vector Machines in R Studio" |
"You're looking for a complete Support Vector Machines course that teaches you everything you need to create a SVM model in R, right?You've found the right Support Vector Machines techniques course!How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course.If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Support Vector Machines.Why should you choose this course?This course covers all the steps that one should take while solving a business problem through SVM.Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy"
Price: 199.99
|
|
| "Excel Analytics: Linear Regression Analysis in MS Excel" |
"You're looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in Excel, right?You've found the right Linear Regression course!After completing this course you will be able to: Identify the business problem which can be solved using linear regression technique of Machine Learning. Create a linear regression model in Excel and analyze its result. Confidently practice, discuss and understand Machine Learning conceptsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear RegressionWhy should you choose this course?This course covers all the steps that one should take while solving a business problem through linear regression.Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this courseWe are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.What is covered in this course?This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.Below are the course contents of this course on Linear Regression: Section 1 - Basics of StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviation Section 2 - Data PreprocessingIn this section you will learn what actions you need to take a step by step to get the data and thenprepare it for the analysis these steps are very important.We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation. Section 3 - Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don't understandit, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.By the end of this course, your confidence in creating a regression model in R will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------------Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.What is the Linear regression technique of Machine learning?Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value.Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).When there is a single input variable (x), the method is referred to as simple linear regression.When there are multiple input variables, the method is known as multiple linear regression.Why learn Linear regression technique of Machine learning?There are four reasons to learn Linear regression technique of Machine learning:1. Linear Regression is the most popular machine learning technique2. Linear Regression has fairly good prediction accuracy3. Linear Regression is simple to implement and easy to interpret4. It gives you a firm base to start learning other advanced techniques of Machine LearningHow much time does it take to learn Linear regression technique of machine learning?Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 4 parts:Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning modelProgramming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the R environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in RUnderstanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture in R where we actually run each query with you."
Price: 199.99
|
|
| "Marketing Analytics: Customer Value and Promotion Strategy" |
"You're looking for a complete course that will teach you how to calculate customer value to drive business decisions involving product pricing, acquisition cost, marketing cost, and many other parts of the business., right?You've found the right course Customer Valuation for Entrepreneurs and Marketers! This course teaches you everything you need to know about different methods to find customer lifetime value and how to find it in Excel using advanced excel tool.After completing this course you will be able to:Understand the value of your customers to make intelligent decisions on how to spend money acquiring themLearn how to measure customer value based on the customer value conceptUse Monte-Carlo simulation to incorporate uncertainty in customer value modelsConfidently practice, discuss and understand Customer value models used by organizationsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Customer Valuation for Entrepreneurs and Marketers course.If you are a business manager or an executive, or a student who wants to learn, calculate and apply customer value in real world problems of business, this course will give you a solid base for that by teaching you the most popular Customer lifetime value models and how to implement it.Why should you choose this course?We believe in teaching by example. This course is no exception. Every Sections primary focus is to teach you the concepts through how-to examples. Each section has the following components:Theoretical concepts and use cases of different Customer value modelsStep-by-step instructions on implement such models in excelDownloadable Excel file containing data and solutions used in each lectureClass notes and assignments to revise and practice the conceptsThe practical classes where we create the model is something which differentiates this course from any other course available online.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Analytics and we have used our experience to include the practical aspects of Marketing and data analytics in this courseWe are also the creators of some of the most popular online courses - with over 170,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.What is covered in this course?Understand the value of your customers to make intelligent decisions on how to spend money acquiring them. In this course, we will explore how one can use excel for finding Customer value toFind lifetime customer valueTake data driven decision on deciding acquisition cost, retention efforts etc Let me give you a brief overview of the courseSection 1 - IntroductionIn this section we will learn about the course structureSection 2 - Lifetime Customer ValueIn this section, we will discuss about the basic of concepts of Customer Lifetime value.Section 3 - Variations and Sensitivity AnalysisIn this section you will learn how to create excel model to find lifetime customer value and perform sensitivity analysis to capture variations in lifetime value under different scenarios.Section 4 - Monte Carlo SimulationThis section we will learn the basics and use-cases of Monte Carlo Simulation. We will also use Monte Carlo simulation to find customer value in uncertain scenarios.Some of the examples in this course are from the book Marketing Analytics: Data-Driven Techniques with Microsoft Excel [Winston, Wayne L.]. We suggest this book as reading material for anyone aspiring to be a marketing analyst. I am pretty confident that the course will give you the necessary knowledge and skills to immediately see practical benefits in your work place.Go ahead and click the enroll button, and I'll see you in lesson 1CheersStart-Tech Academy"
Price: 199.99
|
|
| "Complete Machine Learning with R Studio - ML for 2020" |
"You're looking for a complete Machine Learning course that can help you launch a flourishing career in the field of Data Science & Machine Learning, right?You've found the right Machine Learning course!After completing this course you will be able to: Confidently build predictive Machine Learning models to solve business problems and create business strategy Answer Machine Learning related interview questions Participate and perform in online Data Analytics competitions such as Kaggle competitionsCheck out the table of contents below to see what all Machine Learning models you are going to learn.How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning.Why should you choose this course?This course covers all the steps that one should take while solving a business problem through linear regression.Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this courseWe are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.Below is a list of popular FAQs of students who want to start their Machine learning journey-What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 3 parts:Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning modelProgramming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in PythonUnderstanding of models - Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.Why use R for Machine Learning?Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R1. Its a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, its not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means its easy to find answers to questions and community guidance as you work your way through projects in R.5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier and of course, itll also make you a more flexible and marketable employee when youre looking for jobs in data science.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledgeand further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning."
Price: 199.99
|
|
| "Zero to Hero in Microsoft Excel: Complete Excel guide 2020" |
"6 Reasons why you should choose this Excel courseCarefully designed curriculum teaching you only the most used functionalities of Excel in business environmentConcise - you can complete this course within one weekendBusiness related examples and case studiesAmple practice exercises because Excel requires practiceDownloadable resourcesYour queries will be responded by the Instructor himselfStart using Excel to its full potential to become proficient at your Excel tasks today! Either you're new to Excel, or you've played around with it but want to get more comfortable with Excel's advanced features. Either way, this course will be great for you.A Verifiable Certificate of Completion is presented to all students who undertake this Excel course.Why should you choose this course?This is a complete and concise tutorial on MS Excel which can be completed within 6 hours. We know that your time is important and hence we have created this fast paced course without wasting time on irrelevant Excel operations. What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. Instructors of the course have been teaching Data Science and Machine Learning for over a decade. We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:I had an awesome moment taking this course. It broaden my knowledge more on the power use of Excel as an analytical tools. Kudos to the instructor! - SikiruVery insightful, learning very nifty tricks and enough detail to make it stick in your mind. - ArmandOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there is a practice sheet attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. Solution to Assignment is also shared so that you can review your performance. What is covered in this course? This course covers everything you need to crack Excel in the professional work place.Below are the Excel course contents of this complete and concise course on Microsoft Excel:Introduction - In this video, the structure and contents of the course are discussed.Mathematical Functions - This lecture covers Mathematical formulas such as SUM, AVERAGE,RAND, MIN & MAX, SUMPRODUCT.Textual Formulas - This Excel lecture covers Textual formulas such as TRIM, CONCATENATE, SUBSTITUTE, UPPER & LOWER, LENGTH, LEFT, RIGHT & MIDLogical Formulas - This lecture covers Logical formulas such as AND & OR, IF, COUNTIF, SUMIFDate-time (Temporal) Formulas - This lecture covers Date-time related functions such as TODAY & NOW, DAY, MONTH & YEAR, DATEDIF & DAYSLookup Formulas - This Excel lecture covers Lookup formulas such as VLOOKUP, HLOOKUP, INDEX, MATCHData Tools - This lecture covers Data operating tools such as Data Sorting and Filtering, Data validation, Removing duplicates, Importing Data (Text-to-columns)Formatting data and tables - This Excel lecture covers data formatting options such as coloring, changing font, alignments and table formatting options such as adding borders, having highlighted table headers, banded rows etc.Pivot Tables - This Excel lecture covers Pivot tables end-to-end.Charts - This Excel lecture covers charts such as, Bar/ Column chart, Line Chart, Scatter Plot, Pie & Doughnut charts, Statistical Chart - Histogram, Waterfall, SparklinesExcel Shortcuts - This lecture will introduce you to some important shortcuts and teach you how to find out the shortcut for any particular excel operation.Analytics in Excel - This Excel lecture covers the data analytics options available in Excel such as Regression, Solving linear programming problem (Minimization or Maximization problems), What-if (Goal Seek and Scenario Manager)Macros - This lecture covers the process of recording a Macro, running a Macro and creating a button to run a Macro.Bonus Lectures - Waterfall chart in Excel 2016 and previous versions of Excel, Infographics 1: Cool charts, Infographics 2: Cool chartsAnd so much more!By the end of this course, your confidence in using Excel will soar. You'll have a thorough understanding of how to use Microsoft Excel for study or as a career opportunity.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech AcademyFAQ'sWhy learn Microsoft Excel?1. Microsoft Excel helps solve Business Problems2. Microsoft Excel helps you get stuff done3. Microsoft Excel will make you better at your job (no matter what that is)4. Microsoft Excel know-how can instantly increase your job prospects as well as your starting salaryHow much time does it take to learn Microsoft Excel?Microsoft Excel is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Microsoft Excel quickly starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to learn Microsoft Excel quickly.What are the steps I should follow to learn Microsoft Excel? 1. Start learning from the basics of Microsoft Excel. The first 3 sections of the course cover the basics. 2. Once done with the basic try your hands on advanced MS Excel. Next 7 sections cover Advanced Excel topics 3. Next section will help you some cool new tricks of Microsoft Excel. 4. Practice your learning on the exercise provided with every lecture. What is the difference between basic and advanced level of Excel?At Basic level of MS Excel a personCan build excel formulas using: SUM, IF, AVERAGE, COUNT, ROUND Is comfortable building excel formulas to manipulate text and datesUnderstands and can use the Filter and Sort feature of Microsoft Excel.Can create basic charts like Line chart, bar chart and pie chartAt Advanced level of MS Excel a personCan implement Excel LOOKUP Formulas like VLOOKUP, HLOOKUP, Index and MatchCan use conditional and logical formulas like IF, SUMIF, COUNTIF, OR, AND etc.Knows what a Pivot Table is and how to build one.Knows what an add-in is and how to install one.Can record a macro and use it later.Can successfully edit/modify simple recorded macros.Can create advanced charts like Waterfall chart and overlay chart in Microsoft ExcelCan create solve analytics problem using excel solver.Start working proficiently on Microsoft Excel and increase your office productivity.The Authors of this course have several years of corporate experience and hence have curated the course material keeping in mind the requirement of Excel in today's corporate world."
Price: 199.99
|
|
| "Neural Networks in Python: Deep Learning for Beginners" |
"You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in Python, right?You've found the right Neural Networks course!After completing this course you will be able to:Identify the business problem which can be solved using Neural network Models.Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.Create Neural network models in Python using Keras and Tensorflow libraries and analyze their results.Confidently practice, discuss and understand Deep Learning conceptsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.Why should you choose this course?This course covers all the steps that one should take to create a predictive model using Neural Networks.Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.Below are the course contents of this course on ANN:Part 1 - Python basicsThis part gets you started with Python.This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.Part 2 - Theoretical ConceptsThis part will give you a solid understanding of concepts involved in Neural Networks.In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Part 3 - Creating Regression and Classification ANN model in PythonIn this part you will learn how to create ANN models in Python.We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.We also understand the importance of libraries such as Keras and TensorFlow in this part.Part 4 - Data PreprocessingIn this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split. Part 5 - Classic ML technique - Linear RegressionThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don't understandit, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem.By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------------Below are some popular FAQs of students who want to start their Deep learning journey-Why use Python for Deep Learning?Understanding Python is one of the valuable skills needed for a career in Deep Learning.Though it hasnt always been, Python is the programming language of choice for data science. Heres a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggetss annual poll of data scientists most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, its nice to know that employment opportunities are abundant (and growing) as well.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledgeand further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning."
Price: 199.99
|
|
| "Artificial Neural Networks for Business Managers in R Studio" |
"You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in R, right?You've found the right Neural Networks course!After completing this course you will be able to:Identify the business problem which can be solved using Neural network Models.Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.Create Neural network models in R using Keras and Tensorflow libraries and analyze their results.Confidently practice, discuss and understand Deep Learning conceptsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in R Studio without getting too Mathematical.Why should you choose this course?This course covers all the steps that one should take to create a predictive model using Neural Networks.Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.Below are the course contents of this course on ANN:Part 1 - Setting up R studio and R Crash courseThis part gets you started with R.This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R. Part 2 - Theoretical ConceptsThis part will give you a solid understanding of concepts involved in Neural Networks.In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Part 3 - Creating Regression and Classification ANN model in RIn this part you will learn how to create ANN models in R Studio.We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.We also understand the importance of libraries such as Keras and TensorFlow in this part.Part 4 - Data PreprocessingIn this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split.Part 5 - Classic ML technique - Linear RegressionThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don't understandit, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem.By the end of this course, your confidence in creating a Neural Network model in R will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------------Below are some popular FAQs of students who want to start their Deep learning journey-Why use R for Deep Learning?Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Deep learning in R1. Its a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, its not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means its easy to find answers to questions and community guidance as you work your way through projects in R.5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier and of course, itll also make you a more flexible and marketable employee when youre looking for jobs in data science.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledgeand further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning."
Price: 199.99
|
|
| "Marketing Analytics and Retail Business Management" |
"You're looking for a complete course on understanding Retail Analytics to drive business decisions involving production schedules, inventory management, promotional mail optimization, store layouting, estimating right bundle price, customer valuation and many other parts of the business., right?You've found the right Marketing & Retail Analytics: Strategies & Models in Excel! This course teaches you everything you need to know about different forecasting models, Market Basket analysis, RFM (recency, frequency, monetary) analysis, Customer Valuation methods & Price Bundling analysis and how to implement these models in Excel using advanced excel tool.After completing this course you will be able to:Implement forecasting models such as simple linear, simple multiple regression, Additive and multiplicative trend and seasonality model and many more.Perform market basket analysis and calculate lift to derive a store layout that maximizes sales from complementary products. Do RFM (Recency, frequency, and monetary value) analysis to help you maximize profit from promotional mail campaigns.Increase revenue/profit of your firm by implementing revenue / profit maximizing bundle price point using Excel solver Add-inUnderstand the value of your customers to make intelligent decisions on how to spend money acquiring themConfidently practice, discuss and understand different retail analytics models used by organizationsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Marketing & Retail Analytics: Strategies & Models in Excel course.If you are a business manager or an executive, or a student who wants to learn and apply forecasting models in real world problems of business, this course will give you a solid base for that by teaching you the most popular forecasting models and how to implement it.Why should you choose this course?We believe in teaching by example. This course is no exception. Every Sections primary focus is to teach you the concepts through how-to examples. Each section has the following components:Theoretical concepts and use cases of different forecasting modelsStep-by-step instructions on implement forecasting models in excelDownloadable Excel file containing data and solutions used in each lectureClass notes and assignments to revise and practice the conceptsThe practical classes where we create the model for each of these strategies is something which differentiates this course from any other course available online.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Analytics and we have used our experience to include the practical aspects of Marketing and data analytics in this courseWe are also the creators of some of the most popular online courses - with over 170,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.What is covered in this course?Understanding how future sales will change is one of the key information needed by manager to take data driven decisions. In this course, we will explore how one can use forecasting models toSee patterns in time series dataMake forecasts based on modelsLet me give you a brief overview of the courseSection 1 - IntroductionIn this section we will learn about the course structureSection 2 - Basics of ForecastingIn this section, we will discuss about the basic of forecasting and we will also learn the easiest way to create simple linear regression model in ExcelSection 3 - Getting Data Ready for Regression ModelIn this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation.Section 4 - Forecasting using Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables data set are interpreted in the results.Section 5 - Handling Special events like Holiday salesIn this section we will learn how to incorporate effects of Day of Week Effect, Month Effect or any special event such Holidays, pay day etc.Section 6 - Identifying Seasonality & Trend for ForecastingIn this section we will learn about trends and seasonality and how to use the Solver to develop an additive or multiplicative model to estimate trends and seasonality. We will also learn how to use moving averages to eliminate seasonality to easily see trends in sales.Section 7 - Market Basket Analysis and Lift In this section we will learn about market basket analysis and learn how to calculate lift to derive a store layout that maximizes sales from complementary products.Section 8 - Recency, frequency, and monetary value analysisIn this section we will learn techniques to perform RFM (Recency, frequency, and monetary value) analysis to help you maximize profit from promotional mail campaigns.Section 9 - Recency, frequency, and monetary value analysisIn this section we will learn price bundling techniques and learn how to increase revenue/profit of your firm by implementing revenue / profit maximizing price point using Excel solver Add-inSection 10 - Recency, frequency, and monetary value analysisIn this section, we will discuss about the basic of concepts of Customer Lifetime value and learn how to create excel model to find lifetime customer value and perform sensitivity analysis to capture variations in lifetime value under different scenarios.Section 11 - Excel crash courseIf you're new to Excel, or you've played around with it but want to get more comfortable with Excel's advanced features required for this course. Either way, this section will be great for you to revise your rusty excel skills .Some of the examples in this course are from the book Marketing Analytics: Data-Driven Techniques with Microsoft Excel [Winston, Wayne L.]. We suggest this book as reading material for anyone aspiring to be a marketing analyst. I am pretty confident that the course will give you the necessary knowledge and skills to immediately see practical benefits in your work place.Go ahead and click the enroll button, and I'll see you in lesson 1CheersStart-Tech Academy"
Price: 199.99
|
|
| "CNN for Computer Vision with Keras and TensorFlow in Python" |
"You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right?You've found the right Convolutional Neural Networks course!After completing this course you will be able to:Identify the Image Recognition problems which can be solved using CNN Models.Create CNN models in Python using Keras and Tensorflow libraries and analyze their results.Confidently practice, discuss and understand Deep Learning conceptsHave a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.Why should you choose this course?This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 300,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.Below are the course contents of this course on ANN:Part 1 (Section 2)- Python basicsThis part gets you started with Python.This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.Part 2 (Section 3-6) - ANN Theoretical ConceptsThis part will give you a solid understanding of concepts involved in Neural Networks.In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Part 3 (Section 7-11) - Creating ANN model in PythonIn this part you will learn how to create ANN models in Python.We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.We also understand the importance of libraries such as Keras and TensorFlow in this part.Part 4 (Section 12) - CNN Theoretical ConceptsIn this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.Part 5 (Section 13-14) - Creating CNN model in PythonIn this part you will learn how to create CNN models in Python.We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.Part 6 (Section 15-18) - End-to-End Image Recognition project in PythonIn this section we build a complete image recognition project on colored images.We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).By the end of this course, your confidence in creating a Convolutional Neural Network model in Python will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------------Below are some popular FAQs of students who want to start their Deep learning journey-Why use Python for Deep Learning?Understanding Python is one of the valuable skills needed for a career in Deep Learning.Though it hasnt always been, Python is the programming language of choice for data science. Heres a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggetss annual poll of data scientists most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, its nice to know that employment opportunities are abundant (and growing) as well.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledgeand further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning."
Price: 199.99
|
|
| "Image Recognition for Beginners using CNN in R Studio" |
"You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in R, right?You've found the right Convolutional Neural Networks course!After completing this course you will be able to:Identify the Image Recognition problems which can be solved using CNN Models.Create CNN models in R using Keras and Tensorflow libraries and analyze their results.Confidently practice, discuss and understand Deep Learning conceptsHave a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in R without getting too Mathematical.Why should you choose this course?This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 300,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.Below are the course contents of this course on ANN:Part 1 (Section 2)- Setting up R and R Studio with R crash courseThis part gets you started with R.This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R. Part 2 (Section 3-6) - ANN Theoretical ConceptsThis part will give you a solid understanding of concepts involved in Neural Networks.In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Part 3 (Section 7-11) - Creating ANN model in RIn this part you will learn how to create ANN models in R.We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.We also understand the importance of libraries such as Keras and TensorFlow in this part.Part 4 (Section 12) - CNN Theoretical ConceptsIn this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.Part 5 (Section 13-14) - Creating CNN model in RIn this part you will learn how to create CNN models in R.We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.Part 6 (Section 15-18) - End-to-End Image Recognition project in RIn this section we build a complete image recognition project on colored images.We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).By the end of this course, your confidence in creating a Convolutional Neural Network model in R will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------------Below are some popular FAQs of students who want to start their Deep learning journey-Why use R for Deep Learning?Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Deep learning in R1. Its a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, its not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means its easy to find answers to questions and community guidance as you work your way through projects in R.5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier and of course, itll also make you a more flexible and marketable employee when youre looking for jobs in data science.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledgeand further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning."
Price: 199.99
|
|
| "Deep Learning with Keras and Tensorflow in Python and R" |
"You're looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right?You've found the right Neural Networks course!After completing this course you will be able to:Identify the business problem which can be solved using Neural network Models.Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.Create Neural network models in Python and R using Keras and Tensorflow libraries and analyze their results.Confidently practice, discuss and understand Deep Learning conceptsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.Why should you choose this course?This course covers all the steps that one should take to create a predictive model using Neural Networks.Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.Below are the course contents of this course on ANN:Part 1 - Python and R basicsThis part gets you started with Python.This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.Part 2 - Theoretical ConceptsThis part will give you a solid understanding of concepts involved in Neural Networks.In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Part 3 - Creating Regression and Classification ANN model in Python and RIn this part you will learn how to create ANN models in Python.We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.We also understand the importance of libraries such as Keras and TensorFlow in this part.Part 4 - Data PreprocessingIn this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split. By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------------Below are some popular FAQs of students who want to start their Deep learning journey-Why use Python for Deep Learning?Understanding Python is one of the valuable skills needed for a career in Deep Learning.Though it hasnt always been, Python is the programming language of choice for data science. Heres a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggetss annual poll of data scientists most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, its nice to know that employment opportunities are abundant (and growing) as well.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledgeand further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning."
Price: 199.99
|
|
| "Time Series Analysis and Forecasting using Python" |
"You're looking for a complete course on Time Series Forecasting to drive business decisions involving production schedules, inventory management, manpower planning, and many other parts of the business., right?You've found the right Time Series Analysis and Forecasting course. This course teaches you everything you need to know about different forecasting models and how to implement these models in Python.After completing this course you will be able to:Implement time series forecasting models such as AutoRegression, Moving Average, ARIMA, SARIMA etc.Implement multivariate forecasting models based on Linear regression and Neural Networks.Confidently practice, discuss and understand different Forecasting models used by organizationsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Marketing Analytics: Forecasting Models with Excel course.If you are a business manager or an executive, or a student who wants to learn and apply forecasting models in real world problems of business, this course will give you a solid base by teaching you the most popular forecasting models and how to implement it.Why should you choose this course?We believe in teaching by example. This course is no exception. Every Sections primary focus is to teach you the concepts through how-to examples. Each section has the following components:Theoretical concepts and use cases of different forecasting modelsStep-by-step instructions on implement forecasting models in PythonDownloadable Code files containing data and solutions used in each lectureClass notes and assignments to revise and practice the conceptsThe practical classes where we create the model for each of these strategies is something which differentiates this course from any other course available online.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Analytics and we have used our experience to include the practical aspects of Marketing and data analytics in this courseWe are also the creators of some of the most popular online courses - with over 170,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.What is covered in this course?Understanding how future sales will change is one of the key information needed by manager to take data driven decisions. In this course, we will explore how one can use forecasting models toSee patterns in time series dataMake forecasts based on modelsLet me give you a brief overview of the courseSection 1 - IntroductionIn this section we will learn about the course structureSection 2 - Python basicsThis section gets you started with Python.This section will help you set up the python and Jupyter environment on your system and it'll teachyou how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.Section 3 - Basics of Time Series DataIn this section, we will discuss about the basics of time series data, application of time series forecasting and the standard process followed to build a forecasting modelSection 4 - Pre-processing Time Series DataIn this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for modelsSection 5 - Getting Data Ready for Regression ModelIn this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation.Section 6 - Forecasting using Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results.Section 7 - Theoretical ConceptsThis part will give you a solid understanding of concepts involved in Neural Networks.In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Section 8 - Creating Regression and Classification ANN model in PythonIn this part you will learn how to create ANN models in Python.We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.I am pretty confident that the course will give you the necessary knowledge and skills to immediately see practical benefits in your work place.Go ahead and click the enroll button, and I'll see you in lesson 1CheersStart-Tech Academy"
Price: 199.99
|
|
| "Tools for Working From Home - Google Apps, Trello & Zoom" |
"You're looking for a complete course on Work from Home tools to increase your productivity while working remotely, right?You've found the right ""Tools for remote working"" course. This course teaches you about different free tools available to increase your productivity, manage products and effectively communicate with your team.What is covered in this course?Mastering remote work is all about finding the right tools to stay productive and connected. This course will have you and your team synchronized and working in harmony, wherever you happen to be.Let me give you a brief overview of the courseSection 1 - Google DriveGoogle Drive is a cloud storage platform to keep all your files in one secure and centralized location. The remote workers can store and share documents, spreadsheets, and slide presentations. It can be used for reporting on weekly metrics. Additionally, Google Drive files can be synced across devices, so the individuals can view and update them from anywhere.Section 2 - Google DocsGoogle Docs is a word processor included as part of a free, web-based software office suite offered by Google within its Google Drive service.Section 3 - Google SlidesGoogle Slides is a presentation program included as part of a free, web-based software office suite offered by Google within its Google Drive service.Section 4 - Google SheetsGoogle Sheets is a spreadsheet program included as part of a free, web-based software office suite offered by Google within its Google Drive service. It is a modern version of MS Excel.Section 5 - TrelloTrello is about as simple as it gets when it comes to project management, but that simplicity belies incredible organizational and task management power. Trello is built around the notion of bulletin boards. Each board can represent a project, for example. Within each board, teams create lists, which they then populate with cards. The cards can be assigned to specific team members, labeled, stamped with a deadline, and crammed with comments or attachments. The hierarchical nature of the system makes it flexible while still preserving a baseline simplicity.Section 6 - ZoomIf you have big team meetings that include lots of remote workers, Zoom is a video chat application that supports dozens of participants. Large meetings with up to 500 participants are supported as an add-on feature.How this course will help you?If you are a working professional who wants to learn about tools that help you work remotely with efficiency and high productivity, this course will introduce you to the most popular free online tools which will enable you to collaborate and work effectively even at your home.A Verifiable Certificate of Completion is presented to all students who undertake this ""Tools for Working from Home"" course.Our PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content or anything related to any topic, you can always post a question in the course or send us a direct message.I am pretty confident that the course will give you the necessary knowledge and skills to immediately see practical benefits while working remotely.Go ahead and click the enroll button, and I'll see you in lesson 1CheersStart-Tech Academy"
Price: 199.99
|
|
| "Google Data Studio A-Z for Data Visualization and Dashboards" |
"6 Reasons why you should choose this Google Data Studio courseCarefully designed course, teaching you not only how to draw all types of charts in Google Data Studio, but also advanced Data studio specific featuresConcise - you can complete this course within one weekendBusiness related examples and case studiesAmple practice exercises because Data Visualization requires practiceDownloadable resourcesYour queries will be responded by the Instructor himselfStart using Google Data Studio to its full potential to become proficient at Data Visualization and reporting tasks today! Either you're new to Data Visualization, or you've made some charts and graphs using some visualization software such as MS Excel or Tableau. Either way, this course will be great for you.A Verifiable Certificate of Completion is presented to all students who undertake this Google Data Studio course.Why should you choose this course?This is a complete and concise tutorial on Google Data Studio which can be completed within 6 hours. We know that your time is important and hence we have created this fast paced course without wasting time on irrelevant operations. What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. Instructors of the course have been teaching Data Science and Machine Learning for over a decade. We are also the creators of some of the most popular online courses - with over 600,000 enrollments and thousands of 5-star reviews like these ones:I had an awesome moment taking this course. It broaden my knowledge more on the power use of Excel as an analytical tools. Kudos to the instructor! - SikiruVery insightful, learning very nifty tricks and enough detail to make it stick in your mind. - ArmandOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. What is covered in this course? This course covers everything you need to insightful and dynamic reports using Google Data Studio in the professional work place.Below are the course contents of this complete and concise course on Google Data studio:Introduction - In this section, the structure and the contents of the course are discussed. We also discuss the reason to why should we learn Google Data Studio.Theoretical concepts - This lecture covers the prerequisite understanding of key terminologies and concepts before we start to work on Google Data Studio. All charts and tables in Data Studio - We cover all the available chart types one-by-one. It includes Data tables, scorecards, bar charts, time series, pie charts, GeoMaps, pivot tables and many more.Data filter controls - This lecture covers the filtering options that can be given to the report viewers so that each viewer can filter the data and see only what s/he wants to see.Branding the report - Branding a report is a very popular business practice and we will see how we can do it using brand logo and brand colorsEmbedding external content - We can add videos, quizzes, feedback forms, company websites to our report. Yes! It is possible. We will see how in this section.Blending multiple data sets - Real life data is in multiple tables. To plot a graph using data from multiple tables requires data blending. Very Important Section.Report Sharing and Collaborating - This section covers ways in which you can give viewing or editing rights to others. You can also schedule regular reports to the management using Google Data Studio. Report sharing is something where no other Data Visualization tool can beat Google Data Studio.And so much more!By the end of this course, your confidence in using Google data studio will soar. You'll have a thorough understanding of how to use Data Studio for creating insightful dashboards and beautiful reports.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech AcademyFAQ'sWhat you can do with Data Studio? Visualize your data through highly configurable charts and tables. Easily connect to a variety of data sources. Share your insights with your team or with the world. Collaborate on reports with your team. Speed up your report creation process with built-in sample reports.Is Google Data Studio free to use?Google Data Studio is offered completely free by Google.What is the use of Google Data Studio?Google Data Studio gives you everything you need to turn your client's analytics data into informational, easy-to-understand reports through data visualization. The reports are easy to read, easy to share and even customizable to each of your clientsThe Authors of this course have several years of corporate experience and hence have curated the course material keeping in mind the requirement of Data visualization in today's corporate world."
Price: 199.99
|
|
| "Google BigQuery & PostgreSQL : Big Query for Data Analysis" |
"6 Reasons why you should choose this PostgreSQL and BigQuery courseCarefully designed curriculum teaching you everything in SQL that you will need for Data analysis in businessesComprehensive - covers basic and advanced SQL statements in both PostgreSQL and BigQueryBusiness related examples and case studiesAmple practice exercises because SQL requires practiceDownloadable resourcesYour queries will be responded by the Instructor himselfA Verifiable Certificate of Completion is presented to all students who undertake this SQL course.Why should you choose this course?This is a complete tutorial on SQL which can be completed within a weekend. SQL is the most sought after skill for Data analysis roles in all the companies. So whether you want to start a career as a data scientist or just grow you data analysis skills, this course will cover everything you need to know to do that. What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. Instructors of the course have been teaching Data Science and Machine Learning for over a decade. We are also the creators of some of the most popular online courses - with over 400,000 students and thousands of 5-star reviews like these ones:I had an awesome moment taking this course. It broaden my knowledge more on the power use of SQL as an analytical tools. Kudos to the instructor! - SikiruVery insightful, learning very nifty tricks and enough detail to make it stick in your mind. - ArmandOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there is a practice sheet attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. Solution to Assignment is also shared so that you can review your performance. By the end of this course, your confidence in using SQL will soar. You'll have a thorough understanding of how to use SQL for Data analytics as a career opportunity.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech AcademyFAQ'sWhy learn SQL?SQL is the most universal and common used database language.It powers the most commonly used database engines like PostgreSQL, SQL Server, SQLite, and MySQL. Simply put,If you want to access databases then yes, you need to know SQL.It is not really difficult to learn SQL. SQL is not a programming language, its a query language. The primary objective where SQL was created was to give the possibility to common people get interested data from database. It is also an English like language so anyone who can use English at a basic level can write SQL query easily.SQL is one of the most sought-after skills by hiring employers.You can earn good money How much time does it take to learn SQL?SQL is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn SQL quickly starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to learn SQL quickly.What are the steps I should follow to learn SQL?Start learning from the basics of SQL. The first 10 sections of the course cover the basics.Once done with the basics, try your hands on advanced SQL. Next 10 sections cover Advanced topicsPractice your learning on the exercise provided in every section.What's the difference between SQL and PostgreSQL?SQL is a language. Specifically, the ""Structured Query Language""PostgreSQL is one of several database systems, or RDMS (Relational Database Management System). PostgresSQL is one of several RDMS's, others of which are Oracle, Informix, MySQL, and MSQL.All of these RDMSs use SQL as their language. Each of them have minor variations in the ""dialect"" of SQL that they use, but it's all still SQL.What is BigQuery used for?BigQuery is a web service from Google that is used for handling or analyzing big data. It is part of the Google Cloud Platform. As a NoOps (no operations) data analytics service, BigQuery offers users the ability to manage data using fast SQL-like queries for real-time analysis.Is BigQuery free?The first 10GB of storage per month is free and the first 1TB of query per month is free. Post these limits, BigQuery is chargeable.Which is better, PostgreSQL or MySQL?Both are excellent products with unique strengths, and the choice is often a matter of personal preference.PostgreSQL offers overall features for traditional database applications, while MySQL focuses on faster performance for Web-based applications.Open source development will bring more features to subsequent releases of both databases.Who uses these databases?Here are a few examples of companies that use PostgreSQL: Apple, BioPharm, Etsy, IMDB, Macworld, Debian, Fujitsu, Red Hat, Sun Microsystem, Cisco, Skype.Google BigQuery is used by companies such as Spotify, The New York Times, Stack Etc."
Price: 199.99
|
|
| "Linear Regression and Logistic Regression in Python" |
"You're looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in Python, right?You've found the right Linear Regression course!After completing this course you will be able to:Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning.Create a linear regression and logistic regression model in Python and analyze its result.Confidently model and solve regression and classification problemsA Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.Below are the course contents of this course on Linear Regression:Section 1 - Basics of StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviationSection 2 - Python basicThis section gets you started with Python.This section will help you set up the python and Jupyter environment on your system and it'll teachyou how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.Section 3 - Introduction to Machine LearningIn this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.Section 4 - Data PreprocessingIn this section you will learn what actions you need to take a step by step to get the data and thenprepare it for the analysis these steps are very important.We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.Section 5 - Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don't understandit, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.How this course will help you?If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning, which is Linear Regression and Logistic RegregressionWhy should you choose this course?This course covers all the steps that one should take while solving a business problem through linear and logistic regression.Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------------Below is a list of popular FAQs of students who want to start their Machine learning journey-What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.What is the Linear regression technique of Machine learning?Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value.Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).When there is a single input variable (x), the method is referred to as simple linear regression.When there are multiple input variables, the method is known as multiple linear regression.Why learn Linear regression technique of Machine learning?There are four reasons to learn Linear regression technique of Machine learning:1. Linear Regression is the most popular machine learning technique2. Linear Regression has fairly good prediction accuracy3. Linear Regression is simple to implement and easy to interpret4. It gives you a firm base to start learning other advanced techniques of Machine LearningHow much time does it take to learn Linear regression technique of machine learning?Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 4 parts:Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning modelProgramming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in PythonUnderstanding of Linear and Logistic Regression modelling - Having a good knowledge of Linear and Logistic Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.Why use Python for data Machine Learning?Understanding Python is one of the valuable skills needed for a career in Machine Learning.Though it hasnt always been, Python is the programming language of choice for data science. Heres a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggetss annual poll of data scientists most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, its nice to know that employment opportunities are abundant (and growing) as well."
Price: 199.99
|
|
| "Linear Regression and Logistic Regression using R Studio" |
"You're looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in R Studio, right?You've found the right Linear Regression course!After completing this course you will be able to:Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning.Create a linear regression and logistic regression model in R Studio and analyze its result.Confidently practice, discuss and understand Machine Learning conceptsA Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.How this course will help you?If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear RegressionWhy should you choose this course?This course covers all the steps that one should take while solving a business problem through linear regression.Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.Below are the course contents of this course on Linear Regression:Section 1 - Basics of StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviationSection 2 - Python basicThis section gets you started with Python.This section will help you set up the python and Jupyter environment on your system and it'll teachyou how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.Section 3 - Introduction to Machine LearningIn this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.Section 4 - Data PreprocessingIn this section you will learn what actions you need to take a step by step to get the data and thenprepare it for the analysis these steps are very important.We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.Section 5 - Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don't understandit, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------------Below is a list of popular FAQs of students who want to start their Machine learning journey-What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.What is the Linear regression technique of Machine learning?Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value.Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).When there is a single input variable (x), the method is referred to as simple linear regression.When there are multiple input variables, the method is known as multiple linear regression.Why learn Linear regression technique of Machine learning?There are four reasons to learn Linear regression technique of Machine learning:1. Linear Regression is the most popular machine learning technique2. Linear Regression has fairly good prediction accuracy3. Linear Regression is simple to implement and easy to interpret4. It gives you a firm base to start learning other advanced techniques of Machine LearningHow much time does it take to learn Linear regression technique of machine learning?Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 4 parts:Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning modelProgramming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in PythonUnderstanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.Why use Python for data Machine Learning?Understanding Python is one of the valuable skills needed for a career in Machine Learning.Though it hasnt always been, Python is the programming language of choice for data science. Heres a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggetss annual poll of data scientists most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, its nice to know that employment opportunities are abundant (and growing) as well.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledgeand further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning."
Price: 199.99
|
|
| "Machine Learning & Deep Learning in Python & R" |
"You're looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science & Machine Learning, right?You've found the right Machine Learning course!After completing this course you will be able to: Confidently build predictive Machine Learning and Deep Learning models to solve business problems and create business strategy Answer Machine Learning related interview questions Participate and perform in online Data Analytics competitions such as Kaggle competitionsCheck out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn.How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning.Why should you choose this course?This course covers all the steps that one should take while solving a business problem through linear regression.Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this courseWe are also the creators of some of the most popular online courses - with over 600,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.Table of ContentsSection 1 - Python basicThis section gets you started with Python.This section will help you set up the python and Jupyter environment on your system and it'll teachyou how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.Section 2 - R basicThis section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R. Section 3 - Basics of StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviationSection 4 - Introduction to Machine LearningIn this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.Section 5 - Data PreprocessingIn this section you will learn what actions you need to take a step by step to get the data and thenprepare it for the analysis these steps are very important.We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.Section 6 - Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don't understandit, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.Section 7 - Classification ModelsThis section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors.We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don't understandit, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem.Section 8 - Decision treesIn this section, we will start with the basic theory of decision tree then we will create and plot a simple Regression decision tree. Then we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python and RSection 9 - Ensemble techniqueIn this section, we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. We will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.Section 10 - Support Vector MachinesSVM's are unique models and stand out in terms of their concept. In this section, we will discussion about support vector classifiers and support vector machines. Section 11 - ANN Theoretical ConceptsThis part will give you a solid understanding of concepts involved in Neural Networks.In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Section 12 - Creating ANN model in Python and RIn this part you will learn how to create ANN models in Python and R.We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.We also understand the importance of libraries such as Keras and TensorFlow in this part.Section 13 - CNN Theoretical ConceptsIn this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.Section 14 - Creating CNN model in Python and RIn this part you will learn how to create CNN models in Python and R.We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.Section 15 - End-to-End Image Recognition project in Python and RIn this section we build a complete image recognition project on colored images.We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).Section 16 - Pre-processing Time Series DataIn this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for modelsSection 17 - Time Series ForecastingIn this section, you will learn common time series models such as Auto-regression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX. By the end of this course, your confidence in creating a Machine Learning or Deep Learning model in Python and R will soar. You'll have a thorough understanding of how to use ML/ DL models to create predictive models and solve real world business problems.Below is a list of popular FAQs of students who want to start their Machine learning journey-What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.Why use Python for Machine Learning?Understanding Python is one of the valuable skills needed for a career in Machine Learning.Though it hasnt always been, Python is the programming language of choice for data science. Heres a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggetss annual poll of data scientists most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, its nice to know that employment opportunities are abundant (and growing) as well.Why use R for Machine Learning?Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R1. Its a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, its not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means its easy to find answers to questions and community guidance as you work your way through projects in R.5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier and of course, itll also make you a more flexible and marketable employee when youre looking for jobs in data science.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledgeand further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning."
Price: 199.99
|
|
| "Google Data Studio-Visualizacin de Datos y Cuadros de Mando" |
"6 razones por las que deberas elegir este curso de Google Data StudioEs un curso cuidadosamente diseado, que no slo ensea a dibujar todo tipo de grficos en Google Data Studio, sino tambin las caractersticas especficas avanzadas de Data Studio.Conciso - puedes completar este curso en un fin de semana.Ejemplos y estudios de casos relacionados con el comercio.Amplios ejercicios de prctica ya que la visualizacin de datos requiere habilidad.Recursos descargables.Tus preguntas sern respondidas por el propio instructor.Empieza a usar Google Data Studio en todo su potencial para dominar las tareas de Visualizacin de Datos e informes hoy mismo!O bien eres nuevo en la Visualizacin de Datos, o has hecho algunos grficos y tablas usando algn software de visualizacin como MS Excel o Tableau. De cualquier manera, este curso ser genial para ti.Un Certificado de Finalizacin Acreditado se presenta a todos los estudiantes que realicen este curso de Google Data Studio.Por qu deberas elegir este curso?Este es un completo y conciso tutorial sobre Google Data Studio que puede ser completado en 6 horas. Sabemos que tu tiempo es importante y por lo tanto hemos creado este curso de ritmo rpido sin perder tiempo en operaciones irrelevantes.Qu nos hace estar cualificados para ensearle?El curso es impartido por Abhishek y Pukhraj. Los instructores del curso han estado enseando Ciencia de los Datos y Aprendizaje Automtico durante ms de una dcada.Tambin somos los creadores de algunos de los cursos en lnea ms populares - con ms de 600.000 inscripciones y miles de revisiones de 5 estrellas como estas:Tuve un momento increble tomando este curso. Ampla mi conocimiento sobre el uso de Excel como herramienta analtica. Felicidades al instructor! - SikiruMuy perspicaz, aprendiendo trucos muy ingeniosos y suficientes detalles para que se te queden grabados en la mente. - ArmandNuestra PromesaEnsear a nuestros estudiantes es nuestro trabajo y estamos comprometidos con l. Si tienes alguna pregunta sobre el contenido del curso, la hoja de prctica o cualquier cosa relacionada con cualquier tema, siempre puedes publicar una pregunta en el curso o enviarnos un mensaje directo.Qu se cubre en este curso?Este curso cubre todo lo que necesitas para realizar informes perspicaces y dinmicos utilizando Google Data Studio en el lugar de trabajo profesional.A continuacin se presentan los contenidos de este completo y conciso curso de Google Data Studio:Introduccin - En esta seccin, se discute la estructura y los contenidos del curso. Tambin discutimos la razn por la que debemos aprender Google Data Studio.Conceptos tericos - Este curso cubre el pre-requisito de comprensin de los conceptos y terminologas clave antes de empezar a trabajar en Google Data Studio.Todos los grficos y tablas en Data Studio - Cubrimos todos los tipos de grficos disponibles uno por uno. Incluye tablas de datos, tarjetas de puntuacin, grficos de barras, series temporales, grficos circulares, GeoMaps, tablas pivotantes y muchos ms.Controles de filtro de datos - Este curso cubre las opciones de filtrado que se pueden dar a los espectadores del informe para que cada espectador pueda filtrar los datos y ver slo lo que el desea.Ponerle marca al informe - Ponerle marca al informe es una prctica comercial muy popular y veremos cmo podemos hacerlo usando el logo y los colores de la marca.Insertar contenido externo - Podemos agregar videos, pruebas, formularios de retroalimentacin, sitios web de empresas a nuestro informe. S! Es posible. Veremos cmo en esta seccin.Mezclar mltiples conjuntos de datos - Los datos de la vida real estn en mltiples tablas. Para trazar un grfico usando datos de mltiples tablas se requiere la mezcla de datos. Seccin muy importante.Compartir y Colaborar en los informes - Esta seccin cubre las formas en las que puede dar derechos de visualizacin o edicin a otros. Tambin puede programar informes regulares para la administracin usando Google Data Studio. El uso compartido de informes es algo en lo que ninguna otra herramienta de visualizacin de datos puede superar a Google Data Studio.Y mucho ms!Al final de este curso, tu confianza en el uso de Google Data Studio se incrementar. Comprender a fondo cmo utilizar Data Studio para crear cuadros de mando perspicaces y bellos informes.Adelante, haz clic en el botn de inscripcin, y te ver en la leccin 1!SaludosStart-Tech AcademyPreguntas frecuentesQu puedes hacer con Data Studio?Visualizar sus datos a travs de grficos y tablas altamente configurables.Conectarse fcilmente a una variedad de fuentes de datos.Comparta tus conocimientos con tu equipo o con el mundo.Colaborar en los informes con tu equipo.Acelere tu proceso de creacin de informes con informes de muestra incorporados.Es Google Data Studio de uso gratuito?Google Data Studio es ofrecido completamente gratis por Google.Para qu sirve Google Data Studio?Google Data Studio le ofrece todo lo que necesita para convertir los datos analticos de tu cliente en informes informativos y fciles de entender mediante la visualizacin de datos. Los informes son fciles de leer, fciles de compartir e incluso personalizables para cada uno de tus clientes.Los autores de este curso tienen varios aos de experiencia corporativa y por lo tanto han curado el material del curso teniendo en cuenta el requisito de la visualizacin de datos en el mundo corporativo de hoy."
Price: 199.99
|
|
| "Pentaho for ETL & Data Integration Masterclass 2020- PDI 9.0" |
"What is ETL?The ETL (extract, transform, load) process is the most popular method of collecting data from multiple sources and loading it into a centralized data warehouse. ETL is an essential component of data warehousing and analytics.Why Pentaho for ETL?Pentaho has phenomenal ETL, data analysis, metadata management and reporting capabilities. Pentaho is faster than other ETL tools (including Talend). Its GUI is easier and takes less time to learn. Pentaho is great for beginners.How much can I earn?In the US, median salary of an ETL developer is $74,835 and in India average salary is Rs. 7,06,902 per year. Accenture, Tata Consultancy Services, Cognizant Technology Solutions, Capgemini, IBM, Infosys etc. are major recruiters for people skilled in ETL tools.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. Instructors of the course have been teaching Data Science and Machine Learning for over a decade. We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:I had an awesome moment taking this course. It broaden my knowledge more on the power use of Excel as an analytical tools. Kudos to the instructor! - SikiruVery insightful, learning very nifty tricks and enough detail to make it stick in your mind. - ArmandOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there is a practice sheet attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. Solution to Assignment is also shared so that you can review your performance. By the end of this course, your confidence in using Excel will soar. You'll have a thorough understanding of how to use Microsoft Excel for study or as a career opportunity.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy"
Price: 199.99
|
|
| "HR Analytics using MS Excel for Human Resource Management" |
"You're looking for a complete course on understanding HR Analytics using Excel to drive business decisions, right?You've found the right HR Analytics using MS Excel course! HR analytics provides scientific support to decision-making concerning a firm's human resources. This course addresses the topic of HR analytics with a practical focus, focusing especially on demystifying analytics for HR managers, from both statistical and computing point of view. After completing this course you will be able to:Use MS excel to create and automate the calculation of HR metricsMake HR Dashboards and understand all the charts that you can draw in ExcelImplement predictive ML models such as simple and multiple linear regression to predict outcomes to real world HR problemsUse pivot tables filtering and sorting options in Excel to summarize and derive information out of the HR datacreate appealing data summaries and dashboards to present the HR story in the most effective wayHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this HR Analytics: Strategies & Models in Excel course.If you are an Human Resources manager or an executive, or a student who wants to learn and apply analytics techniques to real world problems of the HR business function, this course will give you a solid base for that by teaching you the most popular HR analytics models and how to implement it them in MS Excel.Why should you choose this course?We believe in teaching by example. This course is no exception. Every Sections primary focus is to teach you the concepts through how-to examples. Each section has the following components:Theoretical concepts and use cases of different HR modelsStep-by-step instructions on implementing HR models in excelDownloadable Excel files containing data and solutions used in each lectureClass notes and assignments to revise and practice the conceptsThe practical classes where we create the model for each of these strategies is something which differentiates this course from any other course available online.What makes us qualified to teach you?The course is taught by Abhishek (MBA - FMS Delhi, B.Tech - IIT Roorkee) and Pukhraj (MBA - IIM Ahmedabad, B.Tech - IIT Roorkee). As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Analytics and we have used our experience to include the practical aspects of HR analytics in this courseWe are also the creators of some of the most popular online courses - with over 600,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.What is covered in this course?The analysis of data is not the main crux of analytics. It is the interpretation that helps provide insights after application of analytical techniques that makes analytics such an important discipline. We have used the most popular analytics software tool which is MS Excel. This will aid the students who have no prior coding background to learn and implement analytics concepts to actually solve real-world HR problems.Let me give you a brief overview of the coursePart 1 - IntroductionIn this section we will learn about the course structure and the meaning of some key terms associated with HR Analytics.Part 2 - Essential MS Excel formulas and using them to calculate HR metricsIn this part, we will start with a tutorial on all the popular MS Excel formulas. Then we will see the implementation of these to calculate and automate the HR metrics. We also discuss a separate case study where we use Excel to calculate the average cost of external and internal hiring.Part 3 - Visualization in Excel and HR DashboardingIn this part, we will begin with a tutorial on all the popular charts and graphs that can be drawn in MS Excel. Then we will see the implementation of these to create visualize HR data. We also discuss a separate case study where we use Excel to build a department wise demographic distribution of human resources.Part 4 - Data summarization using Pivot tablesIn this part, we will learn about several advanced advanced topics in MS Excel such as Pivot tables, indirect functions and also about the data formatting. Then we will see the implementation of these to create beautiful summaries of HR Data. We also discuss a separate case study where we use Excel to build a dynamic department wise demographic dashboard and format it to make it presentable.Part 5 - Basics of Machine Learning and StatisticsIn this part, we introduce the students to the basics of statistics and ML. This part is for students who have no background understanding of ML and statistics conceptsPart 6 - Preprocessing Data for ML modelsIn this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.Part 7 - Linear regression model for predicting metricsThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look discuss an HR case study where we try to predict the CTC to be offered to new recruits basis their previous experience, past CTC, job location and qualification.I am pretty confident that the course will give you the necessary knowledge and skills to immediately see practical benefits in your work place.Go ahead and click the enroll button, and I'll see you in lesson 1CheersStart-Tech Academy"
Price: 199.99
|
|
| "Excel for Financial Analysis and Financial Modeling" |
"How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Financial Analytics: Strategies & Models in Excel course.If you are an Finance manager or an executive, or a student who wants to learn and apply analytics techniques to real world problems of the Finance business function, this course will give you a solid base for that by teaching you the most popular Financial analytics models and how to implement it them in MS Excel.Why should you choose this course?We believe in teaching by example. This course is no exception. Every Sections primary focus is to teach you the concepts through how-to examples. Each section has the following components:Theoretical concepts and use cases of different Financial modelsStep-by-step instructions on implementing Financial models in excelDownloadable Excel files containing data and solutions used in each lectureClass notes and assignments to revise and practice the conceptsThe practical classes where we create the model for each of these strategies is something which differentiates this course from any other course available online.What makes us qualified to teach you?The course is taught by Abhishek (MBA - FMS Delhi, B.Tech - IIT Roorkee) and Pukhraj (MBA - IIM Ahmedabad, B.Tech - IIT Roorkee). As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Analytics and we have used our experience to include the practical aspects of HR analytics in this courseWe are also the creators of some of the most popular online courses - with over 600,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.What is covered in this course?The analysis of data is not the main crux of analytics. It is the interpretation that helps provide insights after application of analytical techniques that makes analytics such an important discipline. We have used the most popular analytics software tool which is MS Excel. This will aid the students who have no prior coding background to learn and implement analytics concepts to actually solve real-world HR problems.Let me give you a brief overview of the coursePart 1 - IntroductionIn this section we will learn about the course structure and the meaning of some key terms associated with HR Analytics.Part 2 - Essential MS Excel formulas and using them to calculate Financial metricsIn this part, we will start with a tutorial on all the popular MS Excel formulas. Then we will see the implementation of these to calculate and automate the HR metrics. We also discuss a separate case study where we use Excel to calculate the average cost of external and internal hiring.Part 3 - Visualization in Excel and Financial DashboardingIn this part, we will begin with a tutorial on all the popular charts and graphs that can be drawn in MS Excel. Then we will see the implementation of these to create visualize HR data. We also discuss a separate case study where we use Excel to build a department wise demographic distribution of human resources.Part 4 - Data summarization using Pivot tablesIn this part, we will learn about several advanced advanced topics in MS Excel such as Pivot tables, indirect functions and also about the data formatting. Then we will see the implementation of these to create beautiful summaries of HR Data. We also discuss a separate case study where we use Excel to build a dynamic department wise demographic dashboard and format it to make it presentable.Part 5 - Basics of Machine Learning and StatisticsIn this part, we introduce the students to the basics of statistics and ML. This part is for students who have no background understanding of ML and statistics conceptsPart 6 - Preprocessing Data for ML modelsIn this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.Part 7 - Linear regression model for predicting metricsThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look discuss an HR case study where we try to predict the CTC to be offered to new recruits basis their previous experience, past CTC, job location and qualification.I am pretty confident that the course will give you the necessary knowledge and skills to immediately see practical benefits in your work place.Go ahead and click the enroll button, and I'll see you in lesson 1CheersStart-Tech Academy"
Price: 199.99
|
|
| "Crea una WEB con WORDPRESS ya! Desde 0, fcil y rpido" |
"Bienvenid@! En este curso aprenders a utilizar wordpress desde 0 y a crear la pgina web de tus sueos. En poco tiempo aprenders a utilizar la plataforma y sus herramientas y te convertirs en un experto! Todo desde el nivel ms bsico, un autntico chollo. Te animo a que te inscribas y aproveches la oferta!"
Price: 19.99
|
|
| "JSP (Java Server Pages) Training" |
"Java Server Pages (JSP) is a server-side programming technology that enables the creation of dynamic, platform-independent method for building Web-based applications. JSP have access to the entire family of Java APIs, including the JDBC API to access enterprise databases. This tutorial will teach you how to use Java Server Pages to develop your web applications in simple and easy steps.JSP is essentially a server-side scripting language that helps you to create dynamic, platform-independent method for building applications based on web. One of the original Java web technologies, JavaServer Pages is widely used to build dynamic web pages that connect to the Java backend. JSP is a Java standard technology that enables you to write dynamic, data-driven pages for your Java web applications.JSP is built on top of the Java Servlet specification. The two technologies typically work together, especially in older Java web applications. From a coding perspective, the most obvious difference between them is that with servlets you write Java code and then embed client-side markup (like HTML) into that code, whereas with JSP you start with the client-side script or markup, then embed JSP tags to connect your page to the Java backend. JavaServer Pages (JSP) technology allows you to easily create web content that has both static and dynamic components. JSP technology makes available all the dynamic capabilities of Java Servlet technology but provides a more natural approach to creating static content. By using JSP, you can take input from users through forms present on web page, display records from a database or another source, and can move dynamically from one page to another JSP page present in same file or other file.Since Java Server Pages are built on top of the Java Servlets API, so like Servlets, JSP also has access to all the powerful Enterprise Java APIs, including JDBC, JNDI, EJB, JAXP, etc. JSP pages can be used in combination with servlets that handle the business logic, the model supported by Java servlet template engines. JSP is a complimentary technology to Java Servlet which facilitates the mixing of dynamic and static web contents. JSP is Java's answer to the popular Microsoft's Active Server Pages (ASP). JSP, like ASP, provides a elegant way to mix static and dynamic contents. The main page is written in regular HTML, while special tags are provided to insert pieces of Java programming codes. The business programming logic and the presentation are cleanly separated. This allows the programmers to focus on the business logic, while the web designer to concentrate on the presentation.A JSP page is a text document that contains two types of text: static data, which can be expressed in any text-based format (such as HTML, SVG, WML, and XML), and JSP elements, which construct dynamic content. JSP helps developers to insert java code in HTML pages by using special JSP tags, most of which start with <% tag and ends with %> tag. Developers of programmers write JSP as a normal text file and then attach this JSP code with any other file like html, xml, etc. The recommended file extension for the source file of a JSP page is .jsp. The page can be composed of a top file that includes other files that contain either a complete JSP page or a fragment of a JSP page. The recommended extension for the source file of a fragment of a JSP page is .jspf.The JSP elements in a JSP page can be expressed in two syntaxes, standard and XML, though any given file can use only one syntax. A JSP page in XML syntax is an XML document and can be manipulated by tools and APIs for XML documents. This chapter and Chapters Chapter 7, JavaServer Pages Standard Tag Library through Chapter 9, Scripting in JSP Pages document only the standard syntax. The XML syntax is covered in Chapter 6, JavaServer Pages Documents.Finally, JSP is an integral part of Java EE, a complete platform for enterprise class applications. This means that JSP can play a part in the simplest applications to the most complex and demanding. Uplatz provides this in-depth training on Java Server Pages (JSP) to help you master the most widely used technology for developing web applications.This practical, application-oriented Java JSP training course teaches Java Servlets, JDBC and JSP and shows how to use it to develop simple to complex database-driven Web applications. It is intended for experienced Java (J2SE) programmers who want to build Web applications or J2EE components and systems.This JavaEE JSP training course for web developers & programmers will show you how to build end to end Web applications using JEE best practices, design patterns, and technologies to ensure that you get a performant, scalable JEE/JSP applications. JSP training develops skills to create web pages that display dynamically-generated content.Features of JSPA language for developing JSP pages, which are text-based documents that describe how to process a request and construct a responseAn expression language for accessing server-side objectsMechanisms for defining extensions to the JSP languageJSP technology also contains an API that is used by developers of web containersAdvantages of JSPUsing Javaserver Pages is very simple and like other Java based programs a candidate can learn JSP without having any in depth knowledge or Java related training. Also it can even be implemented by non- Java programmers.Javaserver Pages allows developers to make presentation codes, since the webpages are compiled dynamically into servers.JSP allows web developers to change a specific portion in the template of a page, without affecting the entire application logic.JSP is a portable platform, which means the technology can be used in other web servers and operating system.Javaserver Pages provided implicit exception handling mechanism and compiles pages automatically.Separation of static and dynamic contents: The dynamic contents are generated via programming logic and inserted into the static template. This greatly simplifies the creation and maintenance of web contents.Reuse of components and tag libraries: The dynamic contents can be provided by re-usable components such as JavaBean, Enterprise JavaBean (EJB) and tag libraries - you do not have to re-inventing the wheels.Java's power and portability.JSP (Java Server Pages) - course syllabusTopics coveredIntroduction to WebIntroduction to JSPDirectory StructureLifecycle JSPScripting Elements - part 1Scripting Elements - part 2Scripting Elements - part 3Implicit Object RequestImplicit Project - part 1Implicit Project - part 2Implicit Project (Login) - part 3Implicit Project (Reg) - part 4Implicit Project - part 5Implicit Project (Output) - part 6Directives Page - part 1Directive Page - part 2Directive Include - part 1Directive Include - part 2JSP Action Tag - Usebean - part 1JSP Action Tag - Usebean - part 2JSP Action Tag - Usebean - part 3JSP - Include Action Tag - part 1JSP - Include Action - part 2JSP - Forward Action - part 1JSP - Forward Action - part 2Expression Language - part 1Expression Language (Param) - part 2Expression Language - part 3Expression Language (RequestScope) - part 4Java Bean using Expression Language - part 1Java Bean using Expression Language - part 2Java Bean using Expression Language - part 3JSTL Core - part 1JSTL Core - part 2JSTL Core (URL) - part 3JSTL SQL - part 1JSTL SQL (Update) - part 2JSTL SQL Update - part 3SQL ParamJSTL - Function - part 1JSTL - Function - part 2JSTL - Function - part 3MVC in JSP - part 1MVC in JSP - part 2Detailed-level list of topics covered1. Web ApplicationsServer-Side ProgrammingWeb Protocols and Web ApplicationsRole of Web ServersJava ServletsUsing Tomcat Web serverStructure of a Java Servlet2. Servlets ArchitectureServlets ArchitectureServlet and HttpServletRequest and ResponseReading Request ParametersProducing an HTML ResponseRedirecting the Web ServerDeployment DescriptorsServlets Life CycleRelationship to the Container3. Interactive Web ApplicationsBuilding an HTML InterfaceHTML FormsHandling Form InputApplication ArchitectureSingle-Servlet ModelMultiple-Servlet ModelRouting Servlet ModelTemplate Parsers4. Session ManagementManaging Client StateSessionsSession ImplementationsHttpSessionSession AttributesSession EventsInvalidating Sessions5. Configuration and ContextThe Need for ConfigurationInitialization ParametersProperties FilesJNDI and the Component EnvironmentJDBC Data SourcesWorking with XML Data6. FiltersServlet FiltersUses for FiltersBuilding a FilterFilter Configuration and ContextFilter ChainsDeploying Filters7. Database and SQL FundamentalsRelational Databases and SQLSQL Versions and Code PortabilityDatabase, Schema, Tables, Columns and RowsDDL - Creating and Managing Database ObjectsDML - Retrieving and Managing DataSequencesStored ProceduresResult Sets and CursorsUsing SQL Terminals8. JDBC FundamentalsWhat is the JDBC API?JDBC DriversMaking a ConnectionCreating and Executing a StatementRetrieving Values from a ResultSetSQL and Java DatatypesSQL NULL Versus Java nullCreating and Updating TablesHandling SQL Exceptions and Proper CleanupHandling SQLWarning9. Advanced JDBCSQL Escape SyntaxUsing Prepared StatementsUsing Callable StatementsScrollable Result SetsUpdatable Result SetsTransactionsCommits, Rollbacks, and SavepointsBatch ProcessingAlternatives to JDBC10. Introduction to Row SetsRow Sets in GUI and J2EE programmingAdvantages of RowSetsRowSet SpecializationsUsing CachedRowSets11. JSP ArchitectureJSP ContainersServlet ArchitecturePage TranslationTypes of JSP ContentDirectivesContent TypeBufferingScripting ElementsJSP ExpressionsStandard ActionsCustom Actions and JSTLObjects and ScopesImplicit ObjectsJSP Lifecycle12. Scripting ElementsTranslation of Template ContentScriptletsExpressionsDeclarationsDos and Don'tsImplicit Objects for ScriptletsThe request ObjectThe response ObjectThe out Object13. Interactive JSP ApplicationsHTML FormsReading CGI ParametersJSPs and Java ClassesError HandlingSession ManagementThe Session APICookies and JSP14. Using JavaBeansSeparating Presentation and Business LogicJSP ActionsJavaBeansWorking with Properties<jsp:useBean><jsp:getProperty> and <jsp:setProperty>Using Form Parameters with BeansObjects and ScopesWorking with Vectors15. The Expression Language and the JSTLGoing ScriptlessThe JSP Expression LanguageEL SyntaxType CoercioError HandlingImplicit Objects for ELThe JSP Standard Tag LibraryRole of JSTLThe Core ActionsUsing Beans with JSTLThe Formatting ActionsScripts vs. EL/JSTL16. Advanced JSP FeaturesWeb ComponentsForwardingInclusionPassing ParametersCustom Tag LibrariesTag Library ArchitectureImplementing in Java or JSPThreadsStrategies for Thread SafetyXML and JSP17. JSP for Web ServicesJSP Training Learning ObjectivesExplain the fundamentals of HTML and HTTP in the World Wide Web.Describe JavaServer Pages and their relationship to servlets and J2EE generally.Describe how a JSP is translated into a servlet and processed at runtime.Explain the use of directives on JSPs and outline the principal directives.Implement simple JSPs that use Java code in declarations, expressions and scriptlets.Enumerate and use the implicit objects available to scripting elements.Implement an interactive Web application using HTML forms and JSP.Use Java exception handling and JSP error pages to handle errors in JSP applications.Implement session management for a JSP application.Manage cookies to store client-specific information at various scopes and durations.Use JavaBeans to implement effective interactive JSP applications.Describe custom tags in JSP and explain how they are implemented, both using Java and JSP itself, and how they are used.Discuss threading issues in JSP and describe the use of directives to control how threading is handled.Describe the various uses of XML in JSP applications.Deploy a logical Web application to a Web server in a WAR file.Describe the use of the JSP expression language to simplify dynamic page output.Write JSP expressions and implement JSPs that use them in favor of scripts.Implement JSPs that use basic JSTL actions to simplify presentation logic.Decompose a JSP application design into fine-grained, reusable elements including JavaBeans, custom tag handlers and tag files that use JSTL.Use core JSTL actions to complement standard actions, custom actions, andJSP expressions for seamless, script-free page logic.Direct conditional and iterative processing of page content by looping through ranges of numbers, over elements in a collection, or over tokens in a master string.Set locale and time zone information in JSPs, and use them to correctly format numbers, dates and times for all clients.Use resource bundles to manage application strings, and produce the appropriate strings at runtime for a particular client locale.Locate a data source, query for relational data, and parse result sets.Perform updates, inserts and deletes on relational data using SQL actions.Manage queries and updates in transaction contexts.Derive information from parsed XML content using XPath expressions.Implement conditional processing and loops based on XML information.Apply XSLT transformations to XML content.Implement a simple Web service that reads and writes SOAP.Understand and appreciate the role of Java Servlets in the overall Java 2 Enterprise Edition architecture, and as the best Java solution to HTTP application development.Use request and response objects provided to a servlet to read CGI parameters and to produce an HTML response.Develop interactive Web applications using HTML forms and servlets.Manage complex conversations with HTTP clients using session attributes.Understand the role of JDBC in Java persistence code, and use JDBC for persistence in servlet applications.Preserve portability and ease of administration for a servlet application by parameterizing servlet code, using initialization parameters, properties files, and JNDI.Use JavaBeans classes to share complex business data between components.Implement filters to adapt existing servlets with new features, and to maximize the decomposition of logic between vertical business functions and horizontal facilities.Comparison of JSP vs. similar technologies or conceptsJSP vs. Active Server Pages (ASP)The advantages of JSP are twofold. First, the dynamic part is written in Java, not Visual Basic or other MS specific language, so it is more powerful and easier to use. Second, it is portable to other operating systems and non-Microsoft Web servers.JSP vs. Pure ServletsIt is more convenient to write (and to modify!) regular HTML than to have plenty of println statements that generate the HTML.JSP vs. Server-Side Includes (SSI)SSI is really only intended for simple inclusions, not for ""real"" programs that use form data, make database connections, and the like.JSP vs. JavaScriptJavaScript can generate HTML dynamically on the client but can hardly interact with the web server to perform complex tasks like database access and image processing etc.JSP vs. Static HTMLRegular HTML, of course, cannot contain dynamic information."
Price: 129.99
|
|
| "Power Point: -" |
"- MS Power Point. , - Power Point, . , , , youtube: """" , Power Point ""- "".... , , - , Power Point - . """" "" "" (.., "" ""), , , . Power Point, - "" "" Power Point , , , .. , . (30 + !), , Power Point. - - ( : Amazon, Ozon, , Ridero...). , Power Point. :- ( - ), , "" "", ; - ; ( .. , , ); ; ... , , Power Point;- ( , , ; , , , ; ..) - ..."
Price: 24.99
|
|
| "Distributeur automatique de pizzas, un business semi-auto" |
"Dans ce cours, vous allez suivre pas pas tout ce que j'ai fais pour lancer ce business. Ce n'est pas quelque chose de thorique comme beaucoup de cours sur le business. Ici, vous me suivez depuis la rflexion et pourquoi ce business est intressant jusqu' l'exploitation.La chronologie, les trucs et astuces, les choses ne pas oublier, tous les chiffres, les photos, vous aurez toutes les cls en main pour lancer avec succs votre 1er distributeur automatique de pizza.Pas besoin d'tre pizzaiolo, profitez du fait que cela soit encore peu dvelopp en France et amusez-vous !"
Price: 24.99
|
|
| "Corso Python per principianti" |
"Impara a programmare in Python, il linguaggio di programmazione pi ricercato del momento.Alla fine di questo corso avrai tutte le basi per iniziare a programmare e imparerai anche dei concetti avanzati che trovi raramente nei corsi per principianti.Userai Python per lavorare con messaggi di testo, file CSV, per visualizzare i dati e creerai un progetto funzionante da zero.Che tu sia un novizio della programmazione, o che vuoi scoprire l'affascinante mondo della programmazione, questo corso fa per te. Questo corso non consiste solo nel farti vedere del codice senza capire i principi della programmazione, va oltre, ti sfida a programmare con compiti e verifica il tuo apprendimento con test mirati.Imparerai concetti base come:operatorifunzioniloopcontrollo del flussotuple, dictionary e liste... e molto altro!Concetti avanzati come:list comprehensionmoduloclassi e oggettiselfcostruttoreereditariet e polimorfismo...e molto altro!"
Price: 34.99
|
|
| "Crestron SimplWindows Modules" |
"In diesem SimplWindows Kurs, werden ausfhrlich die Logikbausteine aus der Crestron Modules Symbols Bibliothek erklrt. Er wird die funktion der Logikbausteine erklrt und wie die Logikbausteine Programmiert werden. Den Logikbaustein erklre ich mit einem Beispielprogramm, welches ich euch als Download zur verfgung stelle. Zustzlich gibt es eine kleine Info ber den Logikbausteine als PDF Dokument zum Downloaden.Modules werden laufend erweitert."
Price: 19.99
|
|
| |
|