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Scala for Machine Learning, Second Edition

You're reading from   Scala for Machine Learning, Second Edition Build systems for data processing, machine learning, and deep learning

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787122383
Length 740 pages
Edition 2nd 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 (21) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Data Pipelines 3. Data Preprocessing 4. Unsupervised Learning 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

Summary

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

  • From monadic composition, high-order collection methods for parallelization to configurability and reusability patterns, Scala is the perfect fit to implement data mining and machine learning algorithms for large-scale projects.
  • There are many logical steps required to create and deploy a machine learning model.
  • The implementation of the binomial logistic regression 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 monadic data transformation and stackable traits.

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Scala for Machine Learning, Second Edition - Second Edition
Published in: Sep 2017
Publisher: Packt
ISBN-13: 9781787122383
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