Linear and Logistic RegressionIntroduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
Linear Models for ClassificationExplores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.
Linear Models: Part 2Covers linear models, binary and multi-class classification, and logistic regression with practical examples.
Supervised Learning EssentialsIntroduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
