Mastering Java for Data Science

Table of Contents

1. Data Science using Java
2. Data Processing toolbox
3. Exploratory Data Analysis
4. Supervised Learning: Classification and Regression
5. Unsupervised Learning: clustering and dimensionality reduction
6. Working with text: NLP/IR
7. Gradient Boosting Trees (xgboost)
8. Deep Learning (deeplearning4j)
9. Scaling Data Science
10. Deploying models