Linear Regression

Linear regression draws the best straight line through your data. It is the "hello world" of machine learning.

Build 1.5 hr Python

Prerequisites: Phase 1 (Linear Algebra, Calculus, Optimization), Phase 2 Lesson 1

#Learning Objectives

Derive the gradient descent update rules for mean squared error and implement linear regression from scratch
Compare gradient descent and the normal equation in terms of computational complexity and when to use each
Build a multiple linear regression model with feature standardization and interpret the learned weights
Explain how Ridge regression (L2 regularization) prevents overfitting by penalizing large weights

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.