Linear Regression: Fundamentals and Applications

This lecture covers the fundamentals of linear regression, including univariate and multivariate models, error functions, and the Ordinary Least Squares (OLS) solution. The instructor explains the process of model training, evaluation, and prediction using Scikit-Learn. The lecture also delves into the importance of performance metrics like R², MSE, and MAE in assessing regression models. Additionally, the train-test split procedure is discussed as a method to estimate model performance. Real-world examples and visualizations are used to illustrate the concepts.