Linear and Logistic RegressionIntroduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
Probabilistic Linear RegressionExplores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Linear Models and OverfittingExplores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.
Nonlinear ML AlgorithmsIntroduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
