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Machine Learning with Scala Quick Start Guide

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

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789345070
Length 220 pages
Edition 1st Edition
Languages
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Authors (2):
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Md. Rezaul Karim Md. Rezaul Karim
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Md. Rezaul Karim
Ajay Kumar N Ajay Kumar N
Author Profile Icon Ajay Kumar N
Ajay Kumar N
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Table of Contents (9) Chapters Close

Preface 1. Introduction to Machine Learning with Scala FREE CHAPTER 2. Scala for Regression Analysis 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

Overview of Scala

Scala is a scalable, functional, and object-oriented programming language that is most closely related to Java. However, Scala is designed to be more concise and have features of functional programming languages. For example, Apache Spark, which is written in Scala, is a fast and general engine for large-scale data processing.

Scala's success is due to many factors: it has many tools that enable succinct expression, it is very concise because you need less typing, and it therefore requires less reading, and it offers very good performance as well. This is why Spark has more support for Scala in the sense that more APIs are available that are written in Scala compared to R, Python, and Java. Scala's symbolic operators are easy to read and, compared to Java, most of the Scala codes are comparatively concise and easy to read; Java is too verbose. Functional programming concepts such as pattern matching and higher-order functions are also available in Scala.

The best way to get started with Scala is either using Scala through the Scala build tool (SBT) or to use Scala through an integrated development environment (IDE). Either way, the first important step is downloading, installing, and configuring Scala. However, since Scala runs on Java Virtual Machine (JVM), having Java installed and configured on your machine is a prerequisite. Therefore, I'm not going to cover how to do that. Instead, I will provide some useful links (https://en.wikipedia.org/wiki/Integrated_development_environment).

Just follow the instructions on how to set up both Java and an IDE (for example, IntelliJ IDEA) or build tool (for example, SBT) at https://www.scala-lang.org/download/. If you're using Windows (for example, Windows 10) or Linux (for example, Ubuntu), visit https://www.journaldev.com/7456/download-install-scala-linux-unix-windows. Finally, here are some macOS instructions: http://sourabhbajaj.com/mac-setup/Scala/README.html.

Java programmers normally prefer Scala when they need to add some functional programming flavor to their codes as Scala runs on JVM. There are various other options when it comes to editors. The following are some options to choose from:

  • Scala IDE
  • Scala plugin for Eclipse
  • IntelliJ IDEA
  • Emacs
  • Vim

Eclipse has several advantages using numerous beta plugins and local, remote, and high-level debugging facilities with semantic highlighting and code completion for Scala.

You have been reading a chapter from
Machine Learning with Scala Quick Start Guide
Published in: Apr 2019
Publisher: Packt
ISBN-13: 9781789345070
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