Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Machine Learning for Mobile
Machine Learning for Mobile

Machine Learning for Mobile: Practical guide to building intelligent mobile applications powered by machine learning

Arrow left icon
Profile Icon Revathi Gopalakrishnan Profile Icon Avinash Venkateswarlu
Arrow right icon
$24.99 $35.99
eBook Dec 2018 274 pages 1st Edition
eBook
$24.99 $35.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Revathi Gopalakrishnan Profile Icon Avinash Venkateswarlu
Arrow right icon
$24.99 $35.99
eBook Dec 2018 274 pages 1st Edition
eBook
$24.99 $35.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$24.99 $35.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR
Modal Close icon
Payment Processing...
tick Completed

Billing Address

Table of content icon View table of contents Preview book icon Preview Book

Machine Learning for Mobile

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...

Introduction to supervised learning algorithms

Let's look at supervised learning for simple day-to-day activities. A parent asks their 15-year-old son to go to the store and get some vegetables. They give him a list of vegetables, say beets, carrots, beans, and tomatoes, that they want him to buy. He goes to the store and is able to identify the list of vegetables as per the list provided by his mother from all the other numerous varieties of vegetables present in the store and put them in his cart before going to the checkout. How was this possible?

Simple. The parent had provided enough training to the son by providing instances of each and every vegetable, which equipped him with sufficient knowledge of the vegetables. The son used the knowledge he has gained to choose the correct vegetables. He used the various attributes of the vegetables to arrive at...

Deep dive into supervised learning algorithms

Assume there are predictor attributes, x1, x2, .... xn, and also an objective attribute, y, for a given dataset. Then, the supervised learning is the machine learning task of finding the prediction function that takes as input both the predictor attributes and the objective attribute from this dataset, and is capable of mapping the predictive attributes to the objective attribute for even unseen data currently not in the training dataset with minimal error.

The data in the dataset used for arriving at the prediction function is called the training data and it consists of a set of training examples where each example consists of an input object, x (typically a vector), and a desired output value, Y. A supervised learning algorithm analyzes the training data and produces an inferred function...

Introduction to unsupervised learning algorithms

Consider a scenario where a child is given a bag full of beads of different sizes, colors, shapes, and made of various materials. We just leave to the child do whatever they want with the whole bag of beads. 

There are various things the child could do, based on their interests:

  • Separate the beads into categories based on size
  • Separate the beads into categories based on shape
  • Separate the beads into categories based on a combination of color and shape
  • Separate the beads into categories based on a combination of material, color, and shape

The possibilities are endless. However, the child without any prior teaching is able to go through the beads and uncover patterns of which it doesn't need any any prior knowledge at all. They are discovering the patterns purely on the basis of going through the beads at hand,...

Deep dive into unsupervised learning algorithms

Unsupervised machine learning deals with learning unlabeled data—that is, data that has not been classified or categorized, and arriving at conclusions/patterns in relation to them.

These categories learn from test data that has not been labeled, classified, or categorized. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data.

The input given to the learning algorithm is unlabeled and, hence, there is no straightforward way to evaluate the accuracy of the structure that is produced as output by the algorithm. This is one feature that distinguishes unsupervised learning from supervised learning

The unsupervised algorithms have predictor attributes but NO objective function...

Summary

In this chapter, we learned about what supervised learning is through a naive example and deep dived into concepts of supervised learning. We went through various supervised learning algorithms with practical examples and their application areas and then we started going through unsupervised learning with naive examples. We also covered the concepts of unsupervised learning and then we went through various unsupervised learning algorithms with practical examples and their application areas.

In the subsequent chapters, we will be solving mobile machine learning problems by using some of the supervised and unsupervised machine learning algorithms that we have gone through in this chapter. We will also be exposing you to mobile machine learning SDKs, which will be used to implement mobile machine learning solutions.

...

References

Left arrow icon Right arrow icon
Download code icon Download Code

Key benefits

  • Build smart mobile applications for Android and iOS devices
  • Use popular machine learning toolkits such as Core ML and TensorFlow Lite
  • Explore cloud services for machine learning that can be used in mobile apps

Description

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.

Who is this book for?

If you are a mobile app developer or a machine learning enthusiast keen to use machine learning to build smart mobile applications, this book is for you. Some experience with mobile application development is all you need to get started with this book. Prior experience with machine learning will be an added bonus

What you will learn

  • Build intelligent machine learning models that run on Android and iOS
  • Use machine learning toolkits such as Core ML, TensorFlow Lite, and more
  • Learn how to use Google Mobile Vision in your mobile apps
  • Build a spam message detection system using Linear SVM
  • Using Core ML to implement a regression model for iOS devices
  • Build image classification systems using TensorFlow Lite and Core ML

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 31, 2018
Length: 274 pages
Edition : 1st
Language : English
ISBN-13 : 9781788621427
Category :
Tools :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR
Modal Close icon
Payment Processing...
tick Completed

Billing Address

Product Details

Publication date : Dec 31, 2018
Length: 274 pages
Edition : 1st
Language : English
ISBN-13 : 9781788621427
Category :
Tools :

Packt Subscriptions

See our plans and pricing
Modal Close icon
$19.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
$199.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts
$279.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total $ 153.97
Machine Learning Projects for Mobile Applications
$43.99
Machine Learning for Mobile
$43.99
Hands-On Machine Learning for Algorithmic Trading
$65.99
Total $ 153.97 Stars icon

Table of Contents

13 Chapters
Introduction to Machine Learning on Mobile Chevron down icon Chevron up icon
Supervised and Unsupervised Learning Algorithms Chevron down icon Chevron up icon
Random Forest on iOS Chevron down icon Chevron up icon
TensorFlow Mobile in Android Chevron down icon Chevron up icon
Regression Using Core ML in iOS Chevron down icon Chevron up icon
The ML Kit SDK Chevron down icon Chevron up icon
Spam Message Detection Chevron down icon Chevron up icon
Fritz Chevron down icon Chevron up icon
Neural Networks on Mobile Chevron down icon Chevron up icon
Mobile Application Using Google Vision Chevron down icon Chevron up icon
The Future of ML on Mobile Applications Chevron down icon Chevron up icon
Question and Answers Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

How do I buy and download an eBook? Chevron down icon Chevron up icon

Where there is an eBook version of a title available, you can buy it from the book details for that title. Add either the standalone eBook or the eBook and print book bundle to your shopping cart. Your eBook will show in your cart as a product on its own. After completing checkout and payment in the normal way, you will receive your receipt on the screen containing a link to a personalised PDF download file. This link will remain active for 30 days. You can download backup copies of the file by logging in to your account at any time.

If you already have Adobe reader installed, then clicking on the link will download and open the PDF file directly. If you don't, then save the PDF file on your machine and download the Reader to view it.

Please Note: Packt eBooks are non-returnable and non-refundable.

Packt eBook and Licensing When you buy an eBook from Packt Publishing, completing your purchase means you accept the terms of our licence agreement. Please read the full text of the agreement. In it we have tried to balance the need for the ebook to be usable for you the reader with our needs to protect the rights of us as Publishers and of our authors. In summary, the agreement says:

  • You may make copies of your eBook for your own use onto any machine
  • You may not pass copies of the eBook on to anyone else
How can I make a purchase on your website? Chevron down icon Chevron up icon

If you want to purchase a video course, eBook or Bundle (Print+eBook) please follow below steps:

  1. Register on our website using your email address and the password.
  2. Search for the title by name or ISBN using the search option.
  3. Select the title you want to purchase.
  4. Choose the format you wish to purchase the title in; if you order the Print Book, you get a free eBook copy of the same title. 
  5. Proceed with the checkout process (payment to be made using Credit Card, Debit Cart, or PayPal)
Where can I access support around an eBook? Chevron down icon Chevron up icon
  • If you experience a problem with using or installing Adobe Reader, the contact Adobe directly.
  • To view the errata for the book, see www.packtpub.com/support and view the pages for the title you have.
  • To view your account details or to download a new copy of the book go to www.packtpub.com/account
  • To contact us directly if a problem is not resolved, use www.packtpub.com/contact-us
What eBook formats do Packt support? Chevron down icon Chevron up icon

Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks? Chevron down icon Chevron up icon
  • You can get the information you need immediately
  • You can easily take them with you on a laptop
  • You can download them an unlimited number of times
  • You can print them out
  • They are copy-paste enabled
  • They are searchable
  • There is no password protection
  • They are lower price than print
  • They save resources and space
What is an eBook? Chevron down icon Chevron up icon

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.