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Scala for Machine Learning

You're reading from   Scala for Machine Learning Leverage Scala and Machine Learning to construct and study systems that can learn from data

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783558742
Length 624 pages
Edition 1st Edition
Languages
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Author (1):
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Patrick R. Nicolas Patrick R. Nicolas
Author Profile Icon Patrick R. Nicolas
Patrick R. Nicolas
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Toc

Table of Contents (15) Chapters Close

Preface 1. Getting Started 2. Hello World! FREE CHAPTER 3. Data Preprocessing 4. Unsupervised Learning 5. Naïve Bayes Classifiers 6. Regression and Regularization 7. Sequential Data Models 8. Kernel Models and Support Vector Machines 9. Artificial Neural Networks 10. Genetic Algorithms 11. Reinforcement Learning 12. Scalable Frameworks A. Basic Concepts Index

What you need for this book

A decent command of the Scala programming language is a prerequisite. Reading through a mathematical formulation, conveniently defined 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 2.10.3 or higher
  • Java JDK 1.7.0_45 or 1.8.0_25
  • SBT 0.13 or higher
  • JFreeChart 1.0.1
  • Apache Commons Math library 3.3 (Chapter 3, Data Preprocessing, Chapter 4, Unsupervised Learning, and Chapter 6, 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 2.2.4 or higher (or Typesafe activator 1.2.10 or higher) (Chapter 12, Scalable Frameworks)
  • Apache Spark 1.0.2 or higher (Chapter 12, Scalable Frameworks)

Note

Understanding the mathematical formulation of a model is optional.

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