Mastering Machine Learning with R, Second Edition

Table of Contents

1. A Process for Success
2. Linear Regression - the Blocking and Tackling of Machine Learning
3. Logistic Regression and Discriminant Analysis
4. Advanced Feature Selection in Linear Models
5. More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
6. Classification and Regression Trees
7. Neural Networks and Deep Learning
8. Cluster Analysis
9. Principal Components Analysis
10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis
11. Creating Ensembles and Multi-Class Classification
12. Time Series and Causality
13. Text Mining
14. R on the Cloud
15. Appendix A: R Fundamentals
16. Appendix B: Sources