Regression: Interactive Lecture

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.