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Artificial Intelligence for Big Data

You're reading from   Artificial Intelligence for Big Data Complete guide to automating Big Data solutions using Artificial Intelligence techniques

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788472173
Length 384 pages
Edition 1st Edition
Languages
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Authors (2):
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Anand Deshpande Anand Deshpande
Author Profile Icon Anand Deshpande
Anand Deshpande
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
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Toc

Table of Contents (14) Chapters Close

Preface 1. Big Data and Artificial Intelligence Systems FREE CHAPTER 2. Ontology for Big Data 3. Learning from Big Data 4. Neural Network for Big Data 5. Deep Big Data Analytics 6. Natural Language Processing 7. Fuzzy Systems 8. Genetic Programming 9. Swarm Intelligence 10. Reinforcement Learning 11. Cyber Security 12. Cognitive Computing 13. Other Books You May Enjoy

Feature extraction

As mentioned earlier in this chapter, the NLP system does not understand string values. They need numerical input to build models, sometimes they are also called numerical features. Feature extraction in NLP is converting a set of text information into a set of numerical features. Any machine learning algorithm that you are going to train would need features in numerical vector forms as it does not understand the string. There are many ways text can be represented as numerical vectors. Some such ways are One hot encoding, TF-IDF, Word2Vec, and CountVectorizer.

One hot encoding

One hot encoding is the binary sparse vector representation of text. In this encoding, the resulting binary vector is all zero-value...

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