Twelve topics: foundations of unsupervised learning and evaluation without labels, distance and similarity, k-means, hierarchical clustering, density-based clustering with DBSCAN and HDBSCAN, probabilistic and soft clustering with Gaussian mixtures and EM, linear dimensionality reduction with PCA, non-linear methods including t-SNE, UMAP and autoencoders, anomaly detection, association rules and frequent patterns, cluster validity and stability, and a closing mastery topic.

Data scientists and analysts who have run k-means and want to know when it is the wrong tool, and interview candidates who need to defend a choice of k or explain why a t-SNE plot is not evidence. It assumes basic probability, vectors and distance. Foundations is a useful prerequisite but not a strict one.

Machine Learning: Foundations covers framing, data preparation and evaluation discipline. Supervised Mastery goes deep on the labelled-data algorithms. Unsupervised Mastery covers clustering, dimensionality reduction and anomaly detection. Ensemble Methods & Mastery covers bagging, boosting and stacking. Foundations first, then any of the other three in whatever order your work or interviews demand.

The course is organised as topics, subtopics, and individual lessons. Each lesson pairs a short set of explanation cards with practice questions written for that lesson, so the questions test the idea you just read rather than a general survey. Wrong answers come with a worked explanation, and the AI guide surfaces the mistakes you keep repeating.