Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.
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.
Covers linear models, binary and multi-class classification, and logistic regression with practical examples.
Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
