Section 1: Introduction to Machine Learning & Data Science. Chapter 1 -- Data Science Overview -- Section 2: Data Preparation & Feature Engineering. Chapter 2 -- Data Preparation Overview. Chapter 3 -- Data Extraction. Chapter 4 -- ML Problem Framing. Chapter 5 -- Data Comprehension. Chapter 6 -- Data Quality Engineering. Chapter 7 -- Feature Optimization. Chapter 8 -- Feature Set Finalization -- Section 3: Build, Train, or Estimate the ML Model. Chapter 9 -- Regression. Chapter 10 -- Classification. Chapter 11 -- Ranking. Chapter 12 -- Clustering. Chapter 13 -- Patterns. Chapter 14 -- Time Series. Chapter 15 -- Anomaly Detection -- Section 4: Model Performance Optimization. Chapter 16 -- Model Optimization & Model Selection. Chapter 17 -- Decision Tree. Chapter 18 -- Ensemble Methods -- Section 5: ML Ethics. Chapter 19 -- ML Ethics -- Section 6: Productionalize the Machine Learning Model. Chapter 20 -- Deploy and Monitor Models.