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You're reading from Machine Learning with Scala Quick Start Guide Leverage popular machine learning algorithms and techniques and implement them in Scala

Product type Paperback

Published in Apr 2019

Publisher Packt

ISBN-13 9781789345070

Length 220 pages

Edition 1st Edition

Languages

Scala

Tools

Construct

Concepts

Machine Learning

Authors (2):

Md. Rezaul Karim

Ajay Kumar N

View More author details

Table of Contents (9) Chapters

Preface

1. Introduction to Machine Learning with Scala

2. Scala for Regression Analysis FREE CHAPTER

3. Scala for Learning Classification

4. Scala for Tree-Based Ensemble Techniques

5. Scala for Dimensionality Reduction and Clustering

6. Scala for Recommender System

7. Introduction to Deep Learning with Scala

8. Other Books You May Enjoy

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Technical requirements

You'll be required to have basic knowledge of Scala and Java. Since Scala is also a JVM-based language, make sure both Java JRE and JDK are installed and configured on your machine. To be more specific, you'll need Scala 2.11.x and Java 1.8.x version installed. Also, you need an IDE, such as Eclipse, IntelliJ IDEA, or Scala IDE, with the necessary plugins. However, if you're using IntelliJ IDEA, Scala will already be integrated.

The code files of this chapter can be found on GitHub:

https://github.com/PacktPublishing/Machine-Learning-with-Scala-Quick-Start-Guide/tree/master/Chapter01

Check out the following video to see the Code in Action:
http://bit.ly/2V3Id08

The rest of the chapter is locked

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Authors (2)

Kumar N

Ajay Kumar N has experience in big data, and specializes in cloud computing and various big data frameworks, including Apache Spark and Apache Hadoop. His primary language of choice is Python, but he also has a special interest in functional programming languages such as Scala. He has worked extensively with NumPy, pandas, and scikit-learn, and often contributes to open source projects related to data science and machine learning.

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Karim

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.

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