Course description

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 concepts


A 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 Regression

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 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 - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching 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 Assignments

With 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 Statistics

This section is divided into five different lectures starting from types of data then types of statistics

then graphical representations to describe the data and then a lecture on measures of center like mean

median and mode and lastly measures of dispersion like range and standard deviation

· Section 2 - R basic

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.

· Section 3 - Introduction to Machine Learning

In 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 Preprocessing

In 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.

· Section 5 - Regression Model

This 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, 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 modeling to create predictive models and solve business problems.

Go ahead and click the enroll button, and I'll see you in lesson 1!

Cheers

Start-Tech Academy

 

Requirements:

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


What you'll learn:

  • 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
  • Indepth 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

Course curriculum

  • 1
    Introduction
  • 2
    Basics of Statistics
    • 003 Types of Data
    • 004 Types of Statistics
    • 005 Describing the data graphically
    • 006 Measures of Centers
    • 008 Measures of Dispersion
  • 3
    Getting started with R and R studio
    • 010 Installing R and R studio
    • 011 Basics of R and R studio
    • 012 Packages in R
    • 013 Inputting data part 1 Inbuilt datasets of R
    • 014 Inputting data part 2 Manual data entry
    • 015 Inputting data part 3 Importing from CSV or Text files
    • 017 Creating Histograms in R
    • 016 Creating Barplots in R
  • 4
    Introduction to Machine Learning
    • 018 Introduction to Machine Learning
    • 019 Building a Machine Learning model
  • 5
    Data Preprocessing
    • 020 Gathering Business Knowledge
    • 022 The Data and the Data Dictionary
    • 023 Importing the dataset into R
    • 021 Data Exploration
    • 025 Univariate Analysis and EDD
    • 026 EDD in R
    • 028 Outlier Treatment
    • 029 Outlier Treatment in R
    • 031 Missing Value imputation
    • 032 Missing Value imputation in R
    • 034 Seasonality in Data
    • 035 Bi-variate Analysis and Variable Transformation
    • 036 Variable transformation in R
    • 038 Non Usable Variables
    • 039 Dummy variable creation Handling qualitative data
    • 040 Dummy variable creation in R
    • 042 Correlation Matrix and cause-effect relationship
    • 043 Correlation Matrix in R
  • 6
    Linear Regression Model
    • 045 The problem statement
    • 046 Basic equations and Ordinary Least Squared (OLS) method
    • 047 Assessing Accuracy of predicted coefficients
    • 048 Assessing Model Accuracy - RSE and R squared
    • 049 Simple Linear Regression in R
    • 051 Multiple Linear Regression
    • 052 The F - statistic
    • 053 Interpreting result for categorical Variable
    • 054 Multiple Linear Regression in R
    • 056 Test-Train split
    • 057 Bias Variance trade-off
    • 058 Test-Train Split in R
  • 7
    Regression models other than OLS
    • 059 Linear models other than OLS
    • 060 Subset Selection techniques
    • 061 Subset selection in R
    • 063 Shrinkage methods - Ridge Regression and The Lasso
    • 064 Ridge regression and Lasso in R
    • 065 Heteroscedasticity

Meet your instructor!

Start-Tech Academy
A technology-based analytics education company


Founded by Abhishek Bansal and Pukhraj Parikh, Start-Tech Academy is a technology-based analytics education company and aims at bringing together the analytics companies and interested Learners. 

Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. 

Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in  MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.

Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.

Take this course today!

"Linear Regression Analysis in R - Machine Learning Basics"

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