Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

Table of Contents

1. Introduction to Machine Learning & Scikit-Learn
2. Making Decisions with Trees
3. Making decisions with linear equations
4. Preparing Your Data
5. Image processing with nearest neighbors
6. Text Classification - Not all data exists in tables
7. Neural Networks - Here comes the Deep Learning
8. Ensembles - When one model is not enough
9. The Y is as important as the X
10. Imbalanced Learn - Not even 1% win the lottery
11. Clustering - Grouping data when no correct answers are provided
12. Anomaly Detection - Finding Outliers in Data
13. Recommender System - Learning about users