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

Norms and Distances

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

Hiring signal: Understanding of norms and distances internals

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: Phase 1, Lessons 01 (Linear Algebra Intuition), 02 (Vectors, Matrices & Operations) Time: ~90 minutes

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 It, Use It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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