Mastering Java for Data Science: Analytics and more for production-ready applications

Key Features

• 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.

Book Description

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.

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

About the Author

Alexey Grigorev is a skilled data scientist, machine learning engineer, and software developer with more than 7 years of professional experience.

He started his career as a Java developer working at a number of large and small companies, but after a while he switched to data science. Right now, Alexey works as a data scientist at Searchmetrics, where, in his day-to-day job, he actively uses Java and Python for data cleaning, data analysis, and modeling.

His areas of expertise are machine learning and text mining, but he also enjoys working on a broad set of problems, which is why he often participates in data science competitions on platforms.

Table of Contents

1. Data Science Using Java
2. Data Processing Toolbox
3. Exploratory Data Analysis
4. Supervised Learning - Classification and Regression
5. Unsupervised Learning - Clustering and Dimensionality Reduction
6. Working with Text - Natural Language Processing and Information Retrieval
7. Extreme Gradient Boosting
8. Deep Learning with DeepLearning4J
9. Scaling Data Science
10. Deploying Data Science Models

Alexey Grigorev is a skilled data scientist, machine learning engineer, and software developer with more than 7 years of professional experience.

He started his career as a Java developer working at a number of large and small companies, but after a while he switched to data science. Right now, Alexey works as a data scientist at Searchmetrics, where, in his day-to-day job, he actively uses Java and Python for data cleaning, data analysis, and modeling.

His areas of expertise are machine learning and text mining, but he also enjoys working on a broad set of problems, which is why he often participates in data science competitions on platforms.