Machine Learning Basics: Linear Regression Model in R Studio

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    Online

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You're looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in R, 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 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 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:
learn what actions you need to take a step by...

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Online

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Different dates availableEnrolment now open

About this course

Learn how to solve real life problem using the Linear Regression technique
Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression
Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm
Understand how to interpret the result of Linear Regression model and translate them into actionable insight
Understanding of basics of statistics and concepts of Machine Learning
In depth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem
Learn advanced variations of OLS method of Linear Regression
Course contains a end-to-end DIY project to implement your learnings from the lectures
How to convert business problem into a Machine learning Linear Regression problem
How to do basic statistical operations in R
Advanced Linear regression techniques using GLMNET package of R
Graphically representing data in R before and after analysis

People pursuing a career in data science
Working Professionals beginning their Data journey
Statisticians needing more practical experience
Anyone curious to master Linear Regression from beginner to advanced in short span of time

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This centre's achievements

2021

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 6 years

Subjects

  • Data analysis
  • Statistics
  • Teaching

Course programme

Introduction 2 lectures 09:14 Welcome to the course! This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Course contents In this lecture you will learn about the contents of this course. This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Introduction 2 lectures 09:14 Welcome to the course! This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Course contents In this lecture you will learn about the contents of this course. This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Welcome to the course! This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Welcome to the course! This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Welcome to the course! This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Welcome to the course! This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Course contents In this lecture you will learn about the contents of this course. This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Course contents In this lecture you will learn about the contents of this course. This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Course contents In this lecture you will learn about the contents of this course. This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Course contents In this lecture you will learn about the contents of this course. This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. In this lecture you will learn about the contents of this course. This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. In this lecture you will learn about the contents of this course. This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Basics of Statistics. 5 lectures 30:10 Types of Data In this lecture you will learn about the different types of data. This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R. Types of Statistics In this lecture you will learn about the types of Statistics. This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding ta using Univariate and Bivariate analysis before running Linear...

Additional information

Students will need to install R and R studio software but we have a separate lecture to help you install the same

Machine Learning Basics: Linear Regression Model in R Studio

£ 10 VAT inc.