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Mastering Java for Data Science

Mastering Java for Data Science

By : Alexey Grigorev
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Mastering Java for Data Science

Mastering Java for Data Science

5 (1)
By: Alexey Grigorev

Overview of this book

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.
Table of Contents (11 chapters)
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Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Here, we create SummaryStatistics objects and add all body content lengths."

A block of code is set as follows:

SummaryStatistics statistics = new SummaryStatistics(); data.stream().mapToDouble(RankedPage::getBodyContentLength)
.forEach(statistics::addValue);
System.out.println(statistics.getSummary());

Any command-line input or output is written as follows:

mvn dependency:copy-dependencies -DoutputDirectory=lib 
mvn compile

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "If, instead, our model outputs some score such that the higher the values of the score the more likely the item is to be positive, then the binary classifier is called a ranking classifier."

Warnings or important notes appear in a box like this.
Tips and tricks appear like this.

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