This is the code repository for Scala Machine Learning Projects, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

Machine learning has made huge impacts on academia and industry by turning data into actionable intelligence. Scala, on the other hand, has been observing a steady rise in adoption over the past few years, especially in the field of data science and analytics. This book is comprehended for data scientists, data engineers and deep learning enthusiasts who have a good background with complex numerical computing and want to know more hands-on machine learning application development.

So If you’re well versed in machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with the popular machine learning libraries such as Spark ML, H2O, MDP, DeepLearning4j, and MXNet.

At the end, you will be able to dominate numerical computing, deep learning, and functional programming to carry out complex numerical tasks to develop, build and deploy research or commercial projects in a production ready environment.

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

The code will look like the following:

def variantId(genotype: Genotype): String = {
val name = genotype.getVariant.getContigName
val start = genotype.getVariant.getStart
val end = genotype.getVariant.getEnd
s"$name:$start:$end"
}

This book is dedicated to developers, data analysts, and deep learning enthusiasts who do not have much background with complex numerical computations but want to know whatdeep learning is. A strong understanding of Scala and its functional programming conceptsis recommended. Some basic understanding and high-level knowledge of Spark ML, H2O, Zeppelin, DeepLearning4j, and MXNet would act as an added advantage in order to grasp this book. Additionally, basic know-how of build tools such as Maven and SBT is assumed. All the examples have been implemented using Scala on an Ubuntu 16.04 LTs 64-bit and Windows 10 64-bit. You will also need the following (preferably the latest versions): Apache Spark 2.0.0 (or higher) MXNet, Zeppelin, DeepLearning4j, and H2O (see the details in the chapter and in the supplied pom.xml files) Hadoop 2.7 (or higher) Java (JDK and JRE) 1.7+/1.8+ Scala 2.11.x (or higher) Eclipse Mars or Luna (latest) with Maven plugin (2.9+), Maven compiler plugin (2.3.2+), and Maven assembly plugin (2.4.1+) IntelliJ IDE SBT plugin and Scala Play Framework installed

• Scala Design Patterns - Second Edition

• Mastering Machine Learning Algorithms

• Machine Learning with the Elastic Stack

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