▶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
▶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
▶Who This Book Is For
Machine Learning for Mobile is for you if you are a mobile developer or machine learning user who aspires to exploit machine learning and use it on mobiles and smart devices. Basic knowledge of machine learning and entry-level experience with mobile application development is preferred.
▶What this book covers
⦁ Chapter 1, Introduction to Machine Learning on Mobile, explains what machine learning is and why we should use it on mobile devices. It introduces different approaches to machine learning and their pro and cons.
⦁ Chapter 2, Supervised and Unsupervised Learning Algorithms, covers supervised and unsupervised approaches of machine learning algorithms. We will also learn about different algorithms, such as Naive Bayes, decision trees, SVM, clustering, associated mapping, and many more.
⦁ Chapter 3, Random Forest on iOS, covers random forests and decision trees in depth and explains how to apply them to solve machine learning problems. We will also create an application using a decision tree to diagnose breast cancer.
⦁ Chapter 4, TensorFlow Mobile in Android, introduces TensorFlow for mobile. We will also learn about the architecture of a mobile machine learning application and write an application using TensorFlow in Android.
⦁ Chapter 5, Regression Using Core ML in iOS, explores regression and Core ML and shows how to apply it to solve a machine learning problem. We will be creating an application using scikit-learn to predict house prices.
⦁ Chapter 6, ML Kit SDK, explores ML Kit and its benefits. We will be creating some image labeling applications using ML Kit and device and cloud APIs.
⦁ Chapter 7, Spam Message Detection in iOS - Core ML, introduces natural language processing and the SVM algorithm. We will solve a problem of bulk SMS, that is, whether messages are spam or not.
⦁ Chapter 8, Fritz, introduces the Fritz mobile machine learning platform. We will create an application using Fritz and Core ML in iOS. We will also see how Fritz can be used with the sample dataset we create earlier in the book.
⦁ Chapter 9, Neural Networks on Mobile, covers the concepts of neural networks, Keras, and their applications in the field of mobile machine learning. We will be creating an application to recognize handwritten digits and also the TensorFlow image recognition model.
⦁ Chapter 10, Mobile Application Using Google Cloud Vision, introduces the Google Cloud Vision label-detection technique in an Android application to determine what is in pictures taken by a camera.
⦁ Chapter 11, Future of ML on Mobile Applications, covers the key features of mobile applications and the opportunities they provide for stakeholders.
⦁ Appendix, Question and Answers, contains questions that may be on your mind and tries to provide answers to those questions.
