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

NLP, subfields, and tasks


Information about the real world exists in the form of structured data, typically generated by automated processes, or unstructured data, which, in the case of text, is created by direct human agency in the form of the written or spoken word. The process of observing real-world situations and using either automated processes or having humans perceive and convert that information into understandable data is very similar in both structured and unstructured data. The transformation of the observed world into unstructured data involves complexities such as the language of the text, the format in which it exists, variances among different observers in interpreting the same data, and so on. Furthermore, the ambiguity caused by the syntax and semantics of the chosen language, subtlety in expression, the context in the data, and so on, make the task of mining text data very difficult.

Next, we will discuss some high-level subfields and tasks that involve NLP and text mining...