Agglomerative

Session 22: Unsupervised Learning, Clustering algorithms, K-means, K-medoids, and Hierarchical

During Sessions 12-21, we discussed an extensive list of supervised learning algorithms. Now, we dive into the realm of unsupervised learning – where we don’t have labels but cluster them based on their attributes. We start with the intuition of unsupervised learning, i.e., how data points in the same class are geometrically close in their feature space. We use this…