Unsupervised Learning & Clustering

Master clustering, dimensionality reduction, and anomaly detection: k-means, DBSCAN, hierarchical methods, PCA, t-SNE, UMAP, Isolation Forest, and PCA reconstruction error. Learn what each algorithm assumes, when to use it, how to choose hyperparameters, and how to evaluate results without ground-truth labels. Build clustering pipelines that actually work in production.

Lessons are AI-assisted and human-reviewed. Learn more.