Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
