Book Image

Mastering Java Machine Learning

By : Uday Kamath, Krishna Choppella
Book Image

Mastering Java Machine Learning

By: Uday Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (20 chapters)
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Summary


A large proportion of information in the digital world is textual. Text mining and NLP are areas concerned with extracting information from this unstructured form of data. Several important sub areas in the field are active topics of research today and an understanding of these areas is essential for data scientists.

Text categorization is concerned with classifying documents into pre-determined categories. Text may be enriched by annotating words, as with POS tagging, in order to give it more structure for subsequent processing tasks to act on. Unsupervised techniques such as clustering can be applied to documents as well. Information extraction and named entity recognition help identify information-rich specifics such as location, person or organization name, and so on. Summarization is another important application for producing concise abstracts of larger documents or sets of documents. Various ambiguities of language and semantics such as context, word sense, and reasoning make...