Mastering Java for Data Science eBook

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An overview of modern Data Science and Machine Learning libraries available in Java
Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks.
Easy-to-follow illustrations and the running example of building a search engine.
Who This Book Is For
This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it.

If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you!

What You Will Learn
Get a solid understanding of the data processing toolbox available in Java
Explore the data science ecosystem available in Java
Find out how to approach different machine learning problems with Java
Process unstructured information such as natural language text or images
Create your own search engine
Get state-of-the-art performance with XGBoost
Learn how to build deep neural networks with DeepLearning4j
Build applications that scale and process large amounts of data
Deploy data science models to production and evaluate their performance
In Detail
Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises.

Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort.

This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data.

Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.

Style and approach
This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples.

This is a digital product.

Mastering Java for Data Science 1st Edition is written by Alexey Grigorev and published by Packt Publishing. The Digital and eTextbook ISBNs for Mastering Java for Data Science are 9781785887390, 1785887394 and the print ISBNs are 9781782174271, 1782174273.