- Scala for Machine Learning Second Edition
- Table of Contents
- Scala for Machine Learning Second Edition
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Customer Feedback
- Preface
- 1. Getting Started
- Mathematical notations for the curious
- Why machine learning?
- Why Scala?
- Model categorization
- Taxonomy of machine learning algorithms
- Leveraging Java libraries
- Tools and frameworks
- Source code
- Let's kick the tires
- Summary
- 2. Data Pipelines
- 3. Data Preprocessing
- 4. Unsupervised Learning
- K-mean clustering
- Expectation-Maximization (EM)
- Summary
- 5. Dimension Reduction
- 6. Naïve Bayes Classifiers
- 7. Sequential Data Models
- 8. Monte Carlo Inference
- 9. Regression and Regularization
- 10. Multilayer Perceptron
- 11. Deep Learning
- 12. Kernel Models and SVM
- 13. Evolutionary Computing
- 14. Multiarmed Bandits
- 15. Reinforcement Learning
- 16. Parallelism in Scala and Akka
- 17. Apache Spark MLlib
- A. Basic Concepts
- B. References
- Index
This book is for software developers with a background in Scala programming who want to learn how to create, validate, and apply machine learning algorithms. The book is also beneficial to data scientists who want to explore functional programming or improve the scalability of their existing applications using Scala.
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