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

Natural Language Processing and information retrieval

Natural Language Processing (NLP) is a part of computer science and computational linguistics that deals with textual data. To a computer, texts are unstructured, and NLP helps find the structure and extract useful information from them.

Information retrieval (IR) is a discipline that studies searching in large unstructured datasets. Typically, these datasets are texts, and the IR systems help users find what they want. Search engines such as Google or Bing are examples of such IR systems: they take in a query and provide a collection of documents ranked according to relevance with respect to the query.

Usually, IR systems use NLP for understanding what the documents are about - so later, when the user needs, these documents can be retrieved. In this chapter, we will go over the basics of text processing for information retrieval. 

Vector Space Model - Bag of Words and TF-IDF

For a computer, a text is just a string of characters with no particular...