Covers the basics of linear regression, binary and multi-class classification, 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 simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.
Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.
Explores Bayesian inference, multiclass classification, logistic regression, and linear regression inference.
Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.
