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.
Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.
