Practical Machine Learning with R

Table of Contents

1. An Introduction to Machine Learning
2. Data Cleaning and Pre-Processing
3. Feature Engineering
4. Introduction to neuralnet and Evaluation Methods
5. Linear and Logistic Regression Models
6. Unsupervised Learning