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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

Regression Using Core ML in iOS

This chapter will provide you with an overview of regression algorithms and insights into the basics of Core ML, and will introduce you to creating a machine learning program leveraging a regression algorithm and predicting the housing price for a given set of housing-related data using Core ML in iOS.

As we already saw in Chapter 1, Introduction to Machine Learning on Mobile, any machine learning program has four phases. We will see what we are going to cover in the four phases and what tools we are going to use to solve the underlying machine learning problem.

Problem definition: The housing information of a certain area is provided and we want to predict the median value of a home in this area.

We will be covering the following topics in the chapter:

  • Understanding what regression is and how to apply it to solve an ML problem
  • Understanding...
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