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About this book

Implement machine learning models in your iOS applications. This short work begins by reviewing the primary principals of machine learning and then moves on to discussing more advanced topics, such as CoreML, the framework used to enable machine learning tasks in Apple products.

Many applications on iPhone use machine learning: Siri to serve voice-based requests, the Photos app for facial recognition, and Facebook to suggest which people that might be in a photo. You'll review how these types of machine learning tasks are implemented and performed so that you can use them in your own apps.

Beginning Machine Learning in iOS is your guide to putting machine learning to work in your iOS applications.

What You'll LearnUnderstand the CoreML components

Train custom models

Implement GPU processing for better computation efficiency

Enable machine learning in your application

Who This Book Is For

Novice developers and programmers who wish to implement machine learning in their iOS applications and those who want to learn the fundamentals about machine learning.

Table of Contents

Chapter 1. Introduction to Machine Learning

Abstract
This chapter provides a basic explanation of the concept of machine learning (ML) along with information about its applications, types of ML, how it works, and why we need it. Even if you are a novice to the concept, this chapter should help you with the information you need to get started with ML. It is a fun chapter, with lots of pictures and examples to help you understand the text.
Mohit Thakkar

Chapter 2. Introduction to Core ML Framework

Abstract
In Chapter 1, you learned the basic definition of machine learning (ML), how it works, where it is used, and what is required to use it. This chapter will introduce you to the Core ML framework that was introduced by Apple Inc. in 2017 to allow application developers to implement ML in their iOS applications. You will also learn the concepts of training and inference, which will give you a clearer picture of how ML works.
Mohit Thakkar

Chapter 3. Custom Core ML Models Using Turi Create

Abstract
In Chapter 2, you learned how to use pretrained machine learning models in your application. In this chapter, you will learn how to train your own model with a third-party framework using Turi Create. You will also find a step-by-step guide on how to convert this trained model into the Core ML format and use it in your application.
Mohit Thakkar

Chapter 4. Custom Core ML Models Using Create ML

Abstract
In Chapter 3, you learned how to train custom machine learning models using a third-party framework called Turi Create. This chapter introduces you to the Create ML framework introduced by Apple Inc. in 2018 to allow developers to train custom ML models for iOS applications. In addition to the image classification model that you trained in Chapter 3, this chapter introduces two more types of models: one trained using text-based data and the other trained using tabular data.
Mohit Thakkar

Chapter 5. Improving Computational Efficiency

Abstract
This chapter shares with the reader’s information on some miscellaneous topics such as the difference between GPU and CPU processing, and things to consider while implementing machine learning.
Mohit Thakkar
Additional information