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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 regression


Regression analysis is a basic method used in the statistical analysis of data. It's a statistical method that helps to find the relationships between variables. It is basically used for understanding the relationship between input and output numerical variables. We should first identify the dependent variable, which will vary based on the value of the independent variable. For example, the value of the house (dependent variable) varies based on the square footage of the house (independent variable). Regression analysis is very useful for prediction.

In a simple regression problem (a single x and a single y), the form of the model would be as follows:

y = A + B*x

In higher dimensions, when we have more than one input (x), the line is called a plane or a hyperplane. 

In our example, we predict the price of the house based on the various parameters that may impact the price of the data in that particular area.

The following are some of the important points to be considered...

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