Covers linear models, binary and multi-class classification, and logistic regression with practical examples.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.
Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.
