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Machine Learning for Mobile

Machine Learning for Mobile

By : Revathi Gopalakrishnan, Avinash Venkateswarlu
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Machine Learning for Mobile

Machine Learning for Mobile

By: Revathi Gopalakrishnan, Avinash Venkateswarlu

Overview of this book

Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples. You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains. By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices.
Table of Contents (14 chapters)
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12
Question and Answers

Summary

In this chapter, we learned about decision trees and random forests, and the differences between them. We also explored a decision tree through a sample dataset and Excel using a sample dataset and used random forest algorithm to it in order to establish the prediction. We used Core ML to write the iOS program, and then we applied the scikit-learn to create the model and converted the scikit model to the Core ML model using Core ML tools.

In the next chapter, we will learn more about TensorFlow and its use in Android. 

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