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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

Suggested online courses

Practical Machine Learning. J. Leek, R. Peng, B. Caffo. Johns Hopkins University, available at https://www.coursera.org/jhu
Probabilistic Graphical Models. D. Koller. Stanford University, available at https://www.coursera.org/course/pgm
Machine Learning. A. Ng. Stanford University, available at https://www.coursera.org/course/ml

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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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