Covers the evaluation of clustering methods, including K-means clustering and the use of evaluation metrics to determine the optimal number of clusters.
Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.
Introduces unsupervised machine learning clustering techniques like K-means, Gaussian Mixture Models, and DBSCAN, explaining their algorithms and applications.
