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

Machine learning for texts

Machine learning plays an important role in text processing. It allows to better understand the information hidden in the text, and extract the useful knowledge hidden there. We are already familiar with machine learning models from the previous chapters, and, in fact, we have even used some of them for texts already, for example, POS tagger and NER from Stanford CoreNLP are all machine learning based models.

In Chapters 4, Supervised Learning - Clasfication and Regression and Chapter 5, Unsupervised Learning - Clustering and Dimensionality Reduction we covered supervised and unsupervised machine learning problems. When it comes to text, both play an important role in helping to organize the texts or extract useful pieces of information. In this section, we will see how to apply them to text data.

Unsupervised learning for texts

As we know, unsupervised machine learning deals with cases when no information about labels is provided. For texts, it means just letting...