- 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
A decent command of the Scala programming language is a prerequisite. Reading through a mathematical formulation, conveniently defied in an information box, is optional. However, some basic knowledge of mathematics and statistics might be helpful to understand the inner workings of some algorithms.
The book uses the following libraries:
- Scala 2.11.8 or higher
- Java 1.8.0_25
- SBT 0.13 or higher
- JFreeChart 1.0.17
- Apache Commons Math library 3.5 (Chapter 3, Data Pre-processing, Chapter 4, Unsupervised Learning, and Chapter 9, Regression and Regularization)
- Indian Institute of Technology Bombay CRF 0.2 (Chapter 7, Sequential Data Models)
- LIBSVM 0.1.6 (Chapter 8, Kernel Models and Support Vector Machines)
- Akka 2.3.8 or higher (or Typesafe activator 1.2.10 or higher) (Chapter 16, Parallelism in Scala and Akka)
- Apache Spark 2.1.0 or higher (Chapter 17, Apache Spark MLlib)
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