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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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 R. Nicolas R. Nicolas
Author Profile Icon R. Nicolas
R. Nicolas
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Table of Contents (20) Chapters Close

Scala for Machine Learning
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started FREE CHAPTER 2. Hello World! 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 Basic Concepts Index

Summary


I 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 and 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 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.

You have been reading a chapter from
Scala for Machine Learning
Published in: Dec 2014
Publisher:
ISBN-13: 9781783558742
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