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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

Solving the problem using regression in Core ML


This section will go through the details of creating a regression model and then using the regression model in an iOS mobile application. It will provide a detailed walk-through of the various steps involved in creating an iOS regression ML application to address the problem defined.

Technical requirements

The following software needs to be installed on the developer machine:

  • Python
  • Xcode in a Mac environment

The exercise programs for this chapter can be downloaded from our GitHub repository at https://github.com/PacktPublishing/Machine-Learning-for-Mobile/tree/master/housing%20price%20prediction

In the following program, we are going to use pandas, numpy, and scikit-learn to create the model. So, install these packages from the pip package manager using the following command from the Command Prompt/Terminal:

pip install scikit-learn
pip install numpy
pip install pandas

In order to convert the created model to the Core ML format, we need to use the...

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