In Part III of this series, we cover the fundamentals of machine learning, focusing on:

• validation methodology (reprint)
• nearest neighbor, k-means, support vector machines, principal component analysis
• tree-based methods: decision trees, bagging, random forest, boosting, XGBoost
• artificial neural networks and deep learning
• reinforcement learning

The focus is on algorithmic development and programming. We code each technique from scratch in Python, using an object-oriented approach.