01. Machine Learning Basics

This source is a comprehensive introduction to machine learning, covering various aspects of the field. It starts by explaining the core concept of learning and its applications in different scenarios. The text then explores different types of machine learning, including supervised, unsupervised, semi-supervised, and reinforcement learning. It dives into specific methods within each category, such as classification, regression, clustering, and association rule learning. Additionally, the source discusses various learning paradigms, including transfer learning, active learning, and ensemble learning. Finally, it emphasizes the importance of choosing the right algorithm for a given problem and highlights the challenges posed by dimensionality reduction and the Rashomon effect.