Book Image

Mastering Java for Data Science

By : Alexey Grigorev
Book Image

Mastering Java for Data Science

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 (17 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback


In this chapter, we talked about unsupervised machine learning and about two common unsupervised learning problems, dimensionality reduction and cluster analysis. We covered the most common algorithms from each type, including PCA and K-means. We also covered the existing implementations of these algorithms in Java, and implemented some of them ourselves. Additionally, we touched some important techniques such as SVD, which are very useful in general.

The previous chapter and this chapter have given us quite a lot of information already. With these chapters, we prepared a good foundation to look at how to process textual data with machine learning and data science algorithm--and this is what we will cover in the next chapter.