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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

Spam Message Detection

This chapter will provide you with an overview of natural language processing (NLP) and discuss how NLP can be combined with machine learning to provide solutions to problems. Then, the chapter will take a real-world use case of doing spam message detection by utilizing NLP, combined with the linear SVM classification model. The program will be implemented as a mobile application using Core ML for iOS.

To handle text in machine learning algorithms, we will go through the various NLP techniques that will be used on the text data to make it ready for learning algorithms. Once the text is prepared, we will see how we can classify it using the linear SVM model. 

Problem definition: The bulk SMS message data is provided, and these messages need to be classified as spam or non-spam messages.

We will be covering the following topics in this...

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