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Mastering Java Machine Learning

You're reading from   Mastering Java Machine Learning A Java developer's guide to implementing machine learning and big data architectures

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785880513
Length 556 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Uday Kamath Uday Kamath
Author Profile Icon Uday Kamath
Uday Kamath
Krishna Choppella Krishna Choppella
Author Profile Icon Krishna Choppella
Krishna Choppella
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Toc

Table of Contents (13) Chapters Close

Preface 1. Machine Learning Review FREE CHAPTER 2. Practical Approach to Real-World Supervised Learning 3. Unsupervised Machine Learning Techniques 4. Semi-Supervised and Active Learning 5. Real-Time Stream Machine Learning 6. Probabilistic Graph Modeling 7. Deep Learning 8. Text Mining and Natural Language Processing 9. Big Data Machine Learning – The Final Frontier A. Linear Algebra B. Probability 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...

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