A Step-by-Step Guide to Linear Regression in Machine Learning

Introduction: In the vast landscape of machine learning, understanding the basics is crucial, and linear regression is an excellent starting point. In this blog post, we'll learn about linear regression by breaking down the concepts step-by-step. But we won't stop at theory, we'll also delve into coding linear regression from scratch, enabling you to understand it from the depth. Step 1: Understanding the Basics At its core, linear regression involves predicting an outcome based on one or more input variables. Imagine trying to predict the score a student might achieve based on the number of hours they study – that's where linear regression comes in. Step 2: The Equation Let's start with equation of a straight line. y = mx + c Here m is the slope/gradient of the line x is the coordinate of datapoint c is the y intercept (where the line crosses the y-axis) Here's how it translates into our student example: Score = Study Hours * Study Efficiency + Baseline Score Here, the student's score