Linear Regression ModelExplores the linear regression model, OLS properties, hypothesis testing, interpretation, transformations, and practical considerations.
NonLinear RegressionExplores non-linear regression models, likelihood estimation, model fitting, and confidence intervals.
Linear Regression BasicsCovers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
Regression IIDelves into regression analysis, emphasizing distributional checks, weighted least squares, and hypothesis testing.
Bayesian Inference: Part 2Explores Bayesian inference, multiclass classification, logistic regression, and linear regression inference.
Linear RegressionCovers linear regression for estimating train speed using least squares and regularization.
