Phase 2: Ensembles & Evaluation · ~90 minutes · Python

Unsupervised Learning

No labels, no teacher. The algorithm finds structure on its own.

What you will learn

Implement K-Means, DBSCAN, and Gaussian Mixture Models from scratch and compare their clustering behavior
Evaluate cluster quality using the silhouette score and the elbow method to select the optimal K
Explain when DBSCAN outperforms K-Means and identify which algorithm handles non-spherical clusters and outliers
Build an anomaly detection pipeline using clustering methods to flag points that deviate from normal patterns

Introduction

Type: Build
Languages: Python
Prerequisites: the Math for AI & ML course
Time: ~90 minutes

Objective

Learning objectives

Implement K-Means, DBSCAN, and Gaussian Mixture Models from scratch and compare their clustering behavior
Evaluate cluster quality using the silhouette score and the elbow method to select the optimal K
Explain when DBSCAN outperforms K-Means and identify which algorithm handles non-spherical clusters and outliers
Build an anomaly detection pipeline using clustering methods to flag points that deviate from normal patterns

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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