Phase 0: Linear Algebra Foundations · ~90 minutes · Python

Norms and Distances

Your distance function defines what "similar" means. Choose wrong and everything downstream breaks.

What you will learn

Implement L1, L2, cosine, Mahalanobis, Jaccard, and edit distance functions from scratch
Select the appropriate distance metric for a given ML task and explain why alternatives fail
Connect L1 and L2 norms to LASSO and Ridge regularization and their geometric constraint regions
Demonstrate how the same dataset produces different nearest neighbors under different metrics

Introduction

Type: Build
Language: Python
Prerequisites: Linear Algebra Intuition
Time: ~90 minutes

Objective

Learning objectives

Implement L1, L2, cosine, Mahalanobis, Jaccard, and edit distance functions from scratch
Select the appropriate distance metric for a given ML task and explain why alternatives fail
Connect L1 and L2 norms to LASSO and Ridge regularization and their geometric constraint regions
Demonstrate how the same dataset produces different nearest neighbors under different metrics

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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