Linear Regression: Basics and Applications

This lecture covers the fundamentals of linear regression, starting with the history of machine learning and progressing to supervised learning. The instructor explains the concepts of building a model, using it to predict new data, and evaluating the model's performance. Through examples like heart disease prediction based on biking and smoking habits, the lecture demonstrates the application of linear regression in real-world scenarios. It also delves into multivariate linear regression, error functions, and the importance of the train-test split procedure for model evaluation.