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

Supervised and Unsupervised Learning Algorithms

In the previous chapter, we got some insight into the various aspects of machine learning and were introduced to the various ways in which machine learning algorithms could be categorized. In this chapter, we will go a step further into machine learning algorithms and try to understand supervised and unsupervised learning algorithms. This categorization is based on the learning mechanism of the algorithm, and is the most popular.

In this chapter, we will be covering the following topics:

  • An introduction to the supervised learning algorithm in the form of a detailed practical example to help understand it and its guiding principles
  • The key supervised learning algorithms and their application areas:
    • Naive Bayes
    • Decision trees
    • Linear regression
    • Logistic regression
    • Support vector machines
    • Random...

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