The repo covers core unsupervised learning methods, including Principal Component Analysis, K-Means clustering, hierarchical clustering, and low-rank matrix completion. It highlights how to reduce dimensionality, measure similarity with distance metrics, explore unlabeled datasets through EDA, and interpret patterns that emerge from the data.