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

Unsupervised Learning

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

Hiring signal: Understanding of unsupervised learning internals

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: Phase 1 (Norms & Distances, Probability & Distributions), Phase 2 Lessons 1-6 Time: ~90 minutes

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

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