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

Leverage the power of machine learning on mobiles and build intelligent mobile applications with ease

Key Features

• 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

Book 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.

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

Who this book is 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

Table of Contents

1. Introduction to Machine Learning on Mobile
2. Supervised and Unsupervised Learning Algorithms
3. Random Forest on iOS
4. Tensor Flow Mobile in Android
5. Regression Using CoreML in iOS
6. ML Kit and Image Labelling
7. Spam Message Detection in iOS - CoreML
8. Fritz - iOS and Android
9. Neural Networks on Mobile
10. Mobile Application using Google Cloud Vision
11. Future of ML on Mobile Applications
12. Appendix

Revathi Gopalakrishnan is a software professional with more than 17 years of experience in the IT industry. She has worked extensively in mobile application development and has played various roles, including developer and architect, and has led various enterprise mobile enablement initiatives for large organizations. She has also worked on a host of consumer applications for various customers around the globe. She has an interest in emerging areas, and machine learning is one of them. Through this book, she has tried to bring out how machine learning can make mobile application development more interesting and super cool. Revathi resides in Chennai and enjoys her weekends with her husband and her two lovely daughters.

Avinash Venkateswarlu has more than 3 years' experience in IT and is currently exploring mobile machine learning. He has worked in enterprise mobile enablement projects and is interested in emerging technologies such as mobile machine learning and cryptocurrency. Venkateswarlu works in Chennai, but enjoys spending his weekends in his home town, Nellore. He likes to do farming or yoga when he is not in front of his laptop exploring emerging technologies.