Phase 0: ML Foundations · ~90 minutes · Python
Linear Regression
Linear regression draws the best straight line through your data. It is the "hello world" of machine learning.
What you will learn
- 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
Introduction
- Type: Build
- Languages: Python
- Prerequisites: the Math for AI & ML course, Optimization
- Time: ~90 minutes
Objective
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
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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