This chapter focuses on unsupervised learning, where the machine learning model discovers patterns and insights from unlabeled data. Students will learn about clustering algorithms, such as k-means and hierarchical clustering, and their applications in areas like customer segmentation, anomaly detection, and feature extraction. They will also explore dimensionality reduction techniques, including principal component analysis (PCA) and t-SNE, and understand their role in data visualization and understanding.