In this series, we cover statistical experimentation, modeling, and machine learning, focusing on:

• basic statistics, hypothesis testing, and experimentation
• validation methodology
• linear and logistic regression; generalized linear models
• causality
• time series analysis
• Bayesian statistics
• clustering
• decision trees, random forests, and boosted forests
• artificial neural networks and deep learning
• reinforcement learning

Our focus is on mathematical derivations, algorithmic development, and programming. We use Python throughout the text to support our theory. All machine learning techniques are coded from scratch using Python.