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Machine Learning for Mobile
Machine Learning for Mobile

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

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Profile Icon Revathi Gopalakrishnan Profile Icon Avinash Venkateswarlu
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Paperback Dec 2018 274 pages 1st Edition
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Arrow left icon
Profile Icon Revathi Gopalakrishnan Profile Icon Avinash Venkateswarlu
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Paperback Dec 2018 274 pages 1st Edition
eBook
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€32.99
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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

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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

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Publication date : Dec 31, 2018
Length: 274 pages
Edition : 1st
Language : English
ISBN-13 : 9781788629355
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Length: 274 pages
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Language : English
ISBN-13 : 9781788629355
Category :
Tools :

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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
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