Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.
Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.
Explores regularization in linear models, including Ridge Regression and the Lasso, analytical solutions, and polynomial ridge regression.
