Book Image

Machine Learning for Mobile

By : Revathi Gopalakrishnan, Avinash Venkateswarlu
Book Image

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 (19 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Question and Answers
Index

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. 

Note

The unsupervised algorithms have predictor attributes but NO objective function.

What does it mean to learn without an objective? Consider the following:

  • Explore the data for natural groupings.
  • Learn association rules, and...