Dimensionality Reduction: PCA & LDACovers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Clustering MethodsCovers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Singular Value DecompositionExplores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.
Machine Learning BasicsIntroduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.