Welcome to the module on ‘Linear Regression’.
In this module, you will start off with a quick introduction to three machine learning techniques: regression, classification and clustering. Then, you will go deeper into one of the most important regression models in machine learning: linear regression.
You will primarily learn about the following two types of linear regression models:
- Simple linear regression.
- Multiple linear regression.
In this session
You will get a quick introduction to machine learning models. You will learn about the basics of simple linear regression and understand how to find the best-fitted line for a model and all the various parameters associated with it.
This session covers the following topics:
- Introduction to machine learning.
- Supervised and unsupervised learning methods.
- The linear regression model.
- Residuals
- Residual sum of squares (RSS) and R² (R-squared).
Guidelines for in-module questions
The in-video and in-content questions for this module are not graded. The graded questions are given in a separate segment at the end of the module. These questions will adhere to the following guidelines:
People you will hear from in this session
Subject Matter Expert
Dinesh J Babu
Associate Professor, IIIT-B
The International Institute of Information Technology, Bangalore, also known as IIIT-B, is one of India’s foremost graduate schools. Through its Integrated M.Tech., M.Tech., M.S. (Research) and PhD programs in the IT space, it focuses equally on innovation and education.