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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

Table of Contents (19) Chapters

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 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 loan. For each individual, the loan amount they offer will vary, based on the different eligibility criteria they satisfy. 

They may go ahead with various decision points to make the final decision to arrive at the possibility of granting a loan and the amount that can be given, such as...

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