Covers the basics of machine learning, supervised and unsupervised learning, various techniques like k-nearest neighbors and decision trees, and the challenges of overfitting.
Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.
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.
