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

Key innovation areas


The following sections detail some of the business areas where innovation is happening, leveraging the power of ML. A number of players are already leading the way in this regard.

Personalization applications

Understanding user behavior by leveraging various parameters that are provided through mobile devices and understanding their life patterns for the purposes of personalization will be of value to users. When the same mobile application is going to cater to user profiles across a broad spectrum, it will be of significant value if it could provide specific features that best suit the person using it. Such advanced personalization could be brought into applications by leveraging ML.

Healthcare

Here, there are various use cases that help track various health parameters that can be tracked, learned, and put into use for providing innovations in healthcare, such as diagnostic applications that can diagnose based on pictures and sound from mobile applications.

Fitness tracking...