Unsupervised Learning
No labels, no teacher. The algorithm finds structure on its own.
Prerequisites: Phase 1 (Norms & Distances, Probability & Distributions), Phase 2 Lessons 1-6
#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
Keep going. The rest of this lesson is one step away.
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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.