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Mastering Java Machine Learning

Mastering Java Machine Learning

By : Kamath, Krishna Choppella
3.4 (9)
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Mastering Java Machine Learning

Mastering Java Machine Learning

3.4 (9)
By: 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 (13 chapters)
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10
A. Linear Algebra
12
Index

Chapter 8. Text Mining and Natural Language Processing

Natural language processing (NLP) is ubiquitous today in various applications such as mobile apps, ecommerce websites, emails, news websites, and more. Detecting spam in e-mails, characterizing e-mails, speech synthesis, categorizing news, searching and recommending products, performing sentiment analysis on social media brands—these are all different aspects of NLP and mining text for information.

There has been an exponential increase in digital information that is textual in content—in the form of web pages, e-books, SMS messages, documents of various formats, e-mails, social media messages such as tweets and Facebook posts, now ranges in exabytes (an exabyte is 1,018 bytes). Historically, the earliest foundational work relying on automata and probabilistic modeling began in the 1950s. The 1970s saw changes such as stochastic modeling, Markov modeling, and syntactic parsing, but their progress was limited...

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