Machine Learning with R

Table of Contents

1. Introducing Machine Learning
2. Managing and Understanding Data
3. Lazy Learning – Classification Using Nearest Neighbors
4. Probabilistic Learning – Classification Using Naive Bayes
5. Divide and Conquer – Classification Using Decision Trees and Rules
6. Forecasting Numeric Data – Regression Methods
7. Black-Box Methods – Neural Networks and Support Vector Machines
8. Finding Patterns – Market Basket Analysis Using Association Rules
9. Finding Groups of Data – Clustering with k-means
10. Evaluating Model Performance
11. Being Successful with Machine Learning
12. Advanced Data Preparation
13. Challenging Data – Too Much, Too Little, Too Complex
14. Building Better Learners
15. Making Use of Big Data