Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Machine Learning for Mobile
  • Toc
  • feedback
Machine Learning for Mobile

Machine Learning for Mobile

By : Revathi Gopalakrishnan, Avinash Venkateswarlu
close
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)
close
12
Question and Answers

Random Forest on iOS

This chapter will provide you with an overview of the random forest algorithm. We will first look at the decision tree algorithm and, once we have a handle on it, try to understand the random forest algorithm. Then, we will use Core ML to create a machine learning program that leverages the random forest algorithm and predicts the possibility of a patient being diagnosed with breast cancer based on a given set of breast cancer patient data.

As we already saw in Chapter 1Introduction to Machine Learning on Mobile, any machine learning program has four phases: define the machine learning problem, prepare the data, build/rebuild/test the model, and deploy it for usage. In this chapter, we will try to relate these with random forest and solve the underlying machine learning problem.

Problem definition: The breast cancer data for certain patients is...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete