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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

Introduction to algorithms

In this section, we will look at the decision tree algorithm. We will go through an example to understand the algorithm. Once we get some clarity on the algorithm, we will try to understand the random forest algorithm with an example.

Decision tree 

To understand the random forest model, we must first learn about the decision tree, the basic building block of a random forest. We all use decision trees in our daily lives, even if you don't know it by that name. You will be able to relate to the concepts of a decision tree once we start going through the example.

Imagine you approach a bank for a loan. The bank will scan you for a series of eligibility criteria before they approve the...

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