Linear Regression and Logistic Regression using R Studio (Udemy.com)
Linear Regression and Logistic Regression for beginners. Understand the difference between Regression & Classification
Created by: Start-Tech Academy
Last updated April 2026
What you will learn
- Learn how to solve real life problem using the Linear and Logistic Regression technique
- Preliminary analysis of data using Univariate and Bivariate analysis before running regression analysis
- Graphically representing data in R before and after analysis
- How to do basic statistical operations in R
- Understand how to interpret the result of Linear and Logistic Regression model and translate them into actionable insight
- Indepth knowledge of data collection and data preprocessing for Linear and Logistic Regression problem
How it compares
Against the other 461 rated Udemy courses in Data Science on CourseDuck:
- Rating4.7 out of 5higher than 85% of them
- Reviews387#262 of 462 by number of reviews
- Length6.3 hoursshorter than 59% of them
- Last updatedApril 2026more recently than 70% of them
Course Description
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 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 - Python basic
This 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 - 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 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!
Cheers
Start-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.
Instructor Details
- 4.7 Rating
387 Reviews
Start-Tech Academy
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.
Founded by Abhishek Bansal and Pukhraj Parikh.
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.
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Reviews
By Viktoriya Vayner on 3/19/2025
I like that this course give a good background for statistics and well as introductory syntax in R. It works very well for people who might need a refresher in statistics and in R but who want to dive into Linear Regression.
By Philip A Koroma on 11/24/2024
I am really learning new stuff from this initial stage. i believe there is more to learn as i progress in this course.
By Craig Burrell on 7/27/2022
This was a relatively low information course that had some rough spots. As an introduction to regression it is serviceable, but as an introduction to regression *in R* it did not inspire confidence. I'm relatively new to R, but even I could see that the R programming in the course is of the crude, slap-dash variety. The instructors don't make use of the tidyverse packages at all, so students are not learning to use the most powerful data science tools that R has to offer. Also, some of the packages ("dummies") that they use in the course no longer work with newer versions of R, so students will have to find substitutes. Some of the lecture segments seem to have been dropped in from other courses (including the last one, which promises to teach about linear discriminant analysis and then ends abruptly).
By Srishti Maini on 5/3/2022
Few tests could be added after every section. They are really helpful. Helps in reminding us what we have learned in the last section.
By Mohit on 4/18/2022
This course is far better than multiple courses on ML (which claims to make you master ML in couple of hours). The instructor explains things which you would definitely encounter in real world scenarios and tells you the ways to solve them. Very realistic and informative course. I would recommend everyone to take up this course which wants to have a good grasp on Linear models.
By Lokesh Kumar on 2/15/2022
Best quality Content easy to understand .Clear concepts valuable course every student who want to learn concept of Machine Learning must join this course .This course will clear all concept . Thanks a lot.
By William T Cole on 1/6/2021
Density is frequency!! 2 semesters of college statistics later and you sir made this concept click, intentionally or not.
By Reyes Barboza Jr on 11/8/2020
Yes. It is practical. The instructor is comfortable relaying the material and this made it easy for me to learn in every session. Thank you!
By Jessica Gaff on 8/9/2020
Great Course! Great intro and background to the stats behind linear and logistic models. A few more examples or practice datasets would be useful though
By Ankita Bagaria on 7/26/2020
I would specially like to mention that this trainer is an excellent instructor. He explains everything so well. This experience is also from one of the SQL courses that I had completed by the same instructor.
Quality Score
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Overall Score : 94 / 100












