Linear and Logistic RegressionIntroduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
Linear Models: ContinuedExplores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
Linear Models: Part 2Covers linear models, binary and multi-class classification, and logistic regression with practical examples.
Nonlinear ML AlgorithmsIntroduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
