This podcast comprehensively covers unsupervised machine learning, focusing on clustering techniques. It explains the theory behind various clustering algorithms—K-Means, hierarchical clustering, and DBSCAN—and provides Python implementations and visualisations for each. Data preparation steps and methods for evaluating clustering performance are also detailed. Finally, the handbook introduces dimensionality reduction using t-SNE for visualising clusters and briefly mentions other unsupervised learning methods such as mixture models and topic modelling.