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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

Summary

In this chapter, we learned about what supervised learning is through a naive example and deep dived into concepts of supervised learning. We went through various supervised learning algorithms with practical examples and their application areas and then we started going through unsupervised learning with naive examples. We also covered the concepts of unsupervised learning and then we went through various unsupervised learning algorithms with practical examples and their application areas.

In the subsequent chapters, we will be solving mobile machine learning problems by using some of the supervised and unsupervised machine learning algorithms that we have gone through in this chapter. We will also be exposing you to mobile machine learning SDKs, which will be used to implement mobile machine learning solutions.

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