Book Image

Machine Learning in Java - Second Edition

By : AshishSingh Bhatia, Bostjan Kaluza
Book Image

Machine Learning in Java - Second Edition

By: AshishSingh Bhatia, Bostjan Kaluza

Overview of this book

As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this book works for JDK 8 and above, the code is tested on JDK 11. Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will have explored related web resources and technologies that will help you take your learning to the next level. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.
Table of Contents (13 chapters)

Introducing text mining

Text mining, or text analytics, refers to the process of automatically extracting high-quality information from text documents, most often written in natural language, where high-quality information is considered to be relevant, novel, and interesting.

While a typical text analytics application is used to scan a set of documents to generate a search index, text mining can be used in many other applications, including text categorization into specific domains; text clustering to automatically organize a set of documents; sentiment analysis to identify and extract subjective information in documents; concept or entity extraction that is capable of identifying people, places, organizations, and other entities from documents; document summarization to automatically provide the most important points in the original document; and learning relations between named...