Machine Learning Basics: Supervised Learning

This lecture covers the basics of machine learning, focusing on supervised learning. It explains the process of data analysis cycle facilitated by machine learning, the types of supervised learning (classification and regression), and various machine learning techniques such as k-NN, Naïve Bayes, and decision trees. The lecture also delves into the concepts of bias and variance tradeoff, model evaluation criteria, and the practical aspects of model selection and overfitting.