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Pascal Bugnion - Scala:Applied Machine Learning

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Pascal Bugnion Scala:Applied Machine Learning

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Leverage the power of Scala and master the art of building, improving, and validating scalable machine learning and AI applications using Scalas most advanced and finest features

About This Book
  • Build functional, type-safe routines to interact with relational and NoSQL databases with the help of the tutorials and examples provided
  • Leverage your expertise in Scala programming to create and customize your own scalable machine learning algorithms
  • Experiment with different techniques; evaluate their benefits and limitations using real-world financial applications
  • Get to know the best practices to incorporate new Big Data machine learning in your data-driven enterprise and gain future scalability and maintainability
Who This Book Is For

This Learning Path is for engineers and scientists who are familiar with Scala and want to learn how to create, validate, and apply machine learning algorithms. It will also benefit software developers with a background in Scala programming who want to apply machine learning.

What You Will Learn
  • Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations
  • Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive
  • Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters
  • Apply key learning strategies to perform technical analysis of financial markets
  • Understand the principles of supervised and unsupervised learning in machine learning
  • Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet
  • Construct reliable and robust data pipelines and manage data in a data-driven enterprise
  • Implement scalable model monitoring and alerts with Scala
In Detail

This Learning Path aims to put the entire world of machine learning with Scala in front of you.

Scala for Data Science, the first module in this course, is a tutorial guide that provides tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed building data science and data engineering solutions.

The second course, Scala for Machine Learning guides you through the process of building AI applications with diagrams, formal mathematical notation, source code snippets, and useful tips. A review of the Akka framework and Apache Spark clusters concludes the tutorial.

The next module, Mastering Scala Machine Learning, is the final step in this course. It will take your knowledge to next level and help you use the knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees.

By the end of this course, you will be a master at Scala machine learning and have enough expertise to be able to build complex machine learning projects using Scala.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

  • Scala for Data Science, Pascal Bugnion
  • Scala for Machine Learning, Patrick Nicolas
  • Mastering Scala Machine Learning, Alex Kozlov
Style and approach

A tutorial with complete examples, this course will give you the tools to start building useful data engineering and data science solutions straightaway. This course provides practical examples from the field on how to correctly tackle data analysis problems, particularly for modern Big Data datasets.

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Scala:Applied Machine Learning

Table of Contents
Scala:Applied Machine Learning

Scala:Applied Machine Learning

Leverage the power of Scala and master the art of building, improving, and validating scalable machine learning and AI applications using Scalas most advanced and finest features

A course in three modules

BIRMINGHAM - MUMBAI ScalaApplied Machine Learning Copyright 2016 Packt - photo 1

BIRMINGHAM - MUMBAI

Scala:Applied Machine Learning

Copyright 2016 Packt Publishing

All rights reserved. No part of this course may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this course to ensure the accuracy of the information presented. However, the information contained in this course is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this course.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this course by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Published on: October 2016

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78712-664-0

www.packtpub.com

Credits

Authors

Pascal Bugnion

Patrick R. Nicolas

Alex Kozlov

Reviewers

Umanga Bista

Radek Ostrowski

Yuanhang Wang

Subhajit Datta

Rui Gonalves

Patricia Hoffman, PhD

Md Zahidul Islam

Rok Kralj

Content Development Editor

Sumeet Sawant

Graphics

Arvindkumar Gupta

Production Coordinator

Arvindkumar Gupta

Preface

Scala is considered to be a successor to Java in the area of Big Data by many. It is particularly good at analyzing large sets of data without any significant impact on performance and thus Scala is being adopted by many developers and data scientists

This Learning Path aims to put the entire world of machine learning with Scala in front of you. We will begin by introducing you to the libraries for ingesting, storing, manipulating, processing, and visualizing data in Scala. Moving on, we'll introduce machine learning in Scala and take a very deep dive into leveraging Scala to construct and study systems that can learn from data. Finally, we will master Scala machine learning in breadth and impart expertise for you to be able to build complex machine learning projects using Scala.

What this learning path covers

, Scala for Data Science , provides you an introduction to the Raspberry Pi .It helps in building games with PyGame and creation of real-life applications with the Raspberry Pi. It further demonstrates the GPIO and cameras with advanced concepts in OpenCV. This module further delves with setting up a web server and creating network utilities.

, Scala for Machine Learning , guides you through the process of building AI applications with diagrams, formal mathematical notation, source code snippets, and useful tips. A review of the Akka framework and Apache Spark clusters concludes the tutorial.

, Mastering Scala Machine Learning , is the final step in this course. It will take your knowledge to next level and help you use the knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees.

What you need for this learning path

You'll need the following set up for all the three modules:

Module 1

The examples provided in this course require that you have a working Scala installation and SBT, the Simple Build Tool , a command line utility for compiling and running Scala code. We will walk you through how to install these in the next sections.

We do not require a specific IDE. The code examples can be written in your favorite text editor or IDE.

Installing the JDK

Scala code is compiled to Java byte code. To run the byte code, you must have the Java Virtual Machine (JVM) installed, which comes as part of a Java Development Kit (JDK). There are several JDK implementations and, for the purpose of this course, it does not matter which one you choose. You may already have a JDK installed on your computer. To check this, enter the following in a terminal:

$ java -versionjava version "1.8.0_66"Java(TM) SE Runtime Environment (build 1.8.0_66-b17)Java HotSpot(TM) 64-Bit Server VM (build 25.66-b17, mixed mode)

If you do not have a JDK installed, you will get an error stating that the java command does not exist.

If you do have a JDK installed, you should still verify that you are running a sufficiently recent version. The number that matters is the minor version number: the 8 in 1.8.0_66. Versions 1.8.xx of Java are commonly referred to as Java 8. For the first twelve chapters of this course, Java 7 will be sufficient (your version number should be something like 1.7.xx or newer). However, you will need Java 8 for the last two chapters, since the Play framework requires it. We therefore recommend that you install Java 8.

On Mac, the easiest way to install a JDK is using Homebrew:

$ brew install java

This will install Java 8, specifically the Java Standard Edition Development Kit, from Oracle.

Homebrew is a package manager for Mac OS X. If you are not familiar with Homebrew, I highly recommend using it to install development tools. You can find installation instructions for Homebrew on: http://brew.sh.

To install a JDK on Windows, go to http://www.oracle.com/technetwork/java/javase/downloads/index.html (or, if this URL does not exist, to the Oracle website, then click on Downloads and download Java Platform, Standard Edition ). Select Windows x86 for 32-bit Windows, or Windows x64 for 64 bit. This will download an installer, which you can run to install the JDK.

To install a JDK on Ubuntu, install OpenJDK with the package manager for your distribution:

$ sudo apt-get install openjdk-8-jdk

If you are running a sufficiently old version of Ubuntu (14.04 or earlier), this package will not be available. In this case, either fall back to openjdk-7-jdk, which will let you run examples in the first twelve chapters, or install the Java Standard Edition Development Kit from Oracle through a PPA (a non-standard package archive):

$ sudo add-apt-repository ppa:webupd8team/java$ sudo apt-get update$ sudo apt-get install oracle-java8-installer

You then need to tell Ubuntu to prefer Java 8 with:

$ sudo update-java-alternatives -s java-8-oracle
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