STATISTICAL LEARNING FOR DATA SCIENCE

Catalog description

Introduces supervised and unsupervised machine learning methods, including linear and nonlinear regression methods, model selection techniques, regularization, tree-based methods, support vector machines, and clustering. Situates these methods within a probabilistic statistical framework. Examines how to compare and assess the performance of prediction and classification methods. Provides the tools to apply these methods in the Python programming language and interpret their output.