Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

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

Table of Contents (13) Chapters Close

Preface 1. Machine Learning Review 2. Practical Approach to Real-World Supervised Learning FREE CHAPTER 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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image