Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.
