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Machine Learning for Mobile

You're reading from  Machine Learning for Mobile

Product type Book
Published in Dec 2018
Publisher Packt
ISBN-13 9781788629355
Pages 274 pages
Edition 1st Edition
Languages
Authors (2):
Revathi Gopalakrishnan Revathi Gopalakrishnan
Profile icon Revathi Gopalakrishnan
Avinash Venkateswarlu Avinash Venkateswarlu
Profile icon Avinash Venkateswarlu
View More author details
Toc

Table of Contents (19) Chapters close

Title Page
Copyright and Credits
About Packt
Contributors
Preface
1. Introduction to Machine Learning on Mobile 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 1. Question and Answers 2. Other Books You May Enjoy Index

Introduction to supervised learning algorithms


Let's look at supervised learning for simple day-to-day activities. A parent asks their 15-year-old son to go to the store and get some vegetables. They give him a list of vegetables, say beets, carrots, beans, and tomatoes, that they want him to buy. He goes to the store and is able to identify the list of vegetables as per the list provided by his mother from all the other numerous varieties of vegetables present in the store and put them in his cart before going to the checkout. How was this possible?

Simple. The parent had provided enough training to the son by providing instances of each and every vegetable, which equipped him with sufficient knowledge of the vegetables. The son used the knowledge he has gained to choose the correct vegetables. He used the various attributes of the vegetables to arrive at the correct class label of the vegetable, which, in this case, is the name of the vegetable. The following table gives us a few of the...

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