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