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Machine Learning for Mobile

You're reading from   Machine Learning for Mobile Practical guide to building intelligent mobile applications powered by machine learning

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781788629355
Length 274 pages
Edition 1st Edition
Tools
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Authors (2):
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Avinash Venkateswarlu Avinash Venkateswarlu
Author Profile Icon Avinash Venkateswarlu
Avinash Venkateswarlu
Revathi Gopalakrishnan Revathi Gopalakrishnan
Author Profile Icon Revathi Gopalakrishnan
Revathi Gopalakrishnan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Machine Learning on Mobile FREE CHAPTER 2. Supervised and Unsupervised Learning Algorithms 3. Random Forest on iOS 4. TensorFlow Mobile in Android 5. Regression Using Core ML in iOS 6. The ML Kit SDK 7. Spam Message Detection 8. Fritz 9. Neural Networks on Mobile 10. Mobile Application Using Google Vision 11. The Future of ML on Mobile Applications 12. Question and Answers 13. Other Books You May Enjoy

Understanding NLP


NLP is a huge topic, and it is beyond the scope of this book to go into detail on the subject. However, in this section, we will go through the high-level details of NLP and try to understand the key concepts required to prepare and process the textual data using NLP, in order to make it ready for consumption by machine learning algorithms for prediction. 

Introducing NLP

Huge, unstructured textual data is getting generated on a daily basis. Social media, websites such as Twitter and Facebook, and communication apps, such as WhatsApp, generate an enormous volume of this unstructured data daily—not to mention the volume created by blogs, news articles, product reviews, service reviews, advertisements, emails, and SMS. So, to summarize, there is huge data (in TBS).

However, it is not possible for a computer to get any insight from this data and to carry out specific actions based on the insights, directly from this huge data, because of the following reasons:

  • The data is unstructured...
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