Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.
Introduces unsupervised machine learning clustering techniques like K-means, Gaussian Mixture Models, and DBSCAN, explaining their algorithms and applications.
Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
