In this episode, we dive into the fundamentals of AI, starting with its basic definition and exploring how it has evolved over time. We discuss the differences between two approaches to AI — rule based systems and machine learning — using examples such as classification for spam filtering and linear regression for house price prediction. We dive deeper into classical machine learning, introducing the concepts of model architecture, features, parameters, objective functions, and training algorithms. We conclude by considering some of the limitations of classical machine learning, and the rise of neural networks.