Lesson 6: Introduction to Unsupervised Learning

Lesson 6: Introduction to Unsupervised Learning Learn how unsupervised learning works and how clustering algorithms find hidden patterns in unlabeled data. In this video, we cover K-Means from scratch with a hand-worked example, then look at how to evaluate clusters and choose the best K. We also build a dendrogram with hierarchical clustering and compare other popular clustering methods. What you'll learn: ✅ The difference between supervised and unsupervised learning ✅ How K-Means works: initialization, assignment, centroid updates and stopping criteria ✅ A full K-Means walkthrough using Euclidean distance ✅ Evaluating clusters with Inertia and the Dunn Index ✅ Choosing the number of clusters with the Elbow Method ✅ Agglomerative vs. divisive hierarchical clustering ✅ Single, complete and average linkage, and how to read a dendrogram ✅ DBSCAN, OPTICS, Mean Shift, Gaussian Mixture Models, Spectral Clustering and BIRCH