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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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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

Summary

We hope you enjoyed this introduction to machine learning and how to leverage your existing skills in Scala programming to create a simple regression program to predict stock price/volume action. Here are the highlights of this introductory chapter:

  • From monadic composition and high-order collection methods for parallelization to configurability to reusability patterns, Scala is the perfect fit to implement and leverage data mining and machine learning algorithms for large-scale projects
  • There are many steps to create and apply a machine learning model
  • The implementation of the logistic binary classifier presented as part of the test case is simple enough to encourage you to learn how to write and apply more advanced machine learning algorithms

To the delight of Scala programming aficionados, the next chapter will dig deeper into building a flexible workflow by leveraging traits and dependency injection.

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