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.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
