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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

Product type Paperback

Published in Dec 2014

Publisher

ISBN-13 9781783558742

Length 624 pages

Edition 1st Edition

Languages

Scala

Concepts

Machine Learning

Author (1):

Patrick R. Nicolas

View More author details

Table of Contents (15) Chapters

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

A. Basic Concepts

Index

Who this book is for

This book is for software developers with a background in Scala programming who want to learn how to create, validate, and apply machine learning algorithms.

The book is also beneficial to data scientists who want to explore functional programming or improve the scalability of their existing applications using Scala.

This book is designed as a tutorial with comparative hands-on exercises using technical analysis of financial markets.

The rest of the chapter is locked

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Authors (1)

R. Nicolas

R. Nicolas

Patrick R. Nicolas is the director of engineering at Agile SDE, California. He has more than 25 years of experience in software engineering and building applications in C++, Java, and more recently in Scala/Spark, and has held several managerial positions. His interests include real-time analytics, modeling, and the development of nonlinear models.

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