Cover
Introduction
1. What Does This Book Cover?
2. Additional Resources
3. Reader Support for This Book
Part 1: Fundamentals of Machine Learning
1. Chapter 1: Introduction to Machine Learning
  1. What Is Machine Learning?
  2. Types of Machine Learning Systems
  3. Common Machine Learning Algorithms
  4. Sources of Machine Learning Datasets
  5. Summary
2. Chapter 2: The Machine-Learning Approach
  1. The Traditional Rule-Based Approach
  2. A Machine-Learning System
  3. The Machine-Learning Process
  4. Summary
3. Chapter 3: Data Exploration and Preprocessing
  1. Data Preprocessing Techniques
  2. Selecting Training Features
  3. Summary
4. Chapter 4: Implementing Machine Learning on Mobile Apps
  1. Device-Based vs. Server-Based Approaches
  2. Apple's Machine Learning Frameworks and Tools
  3. Third-Party Machine-Learning Frameworks and Tools
  4. Summary
Part 2: Machine Learning with CoreML, CreateML, and TuriCreate
1. Chapter 5: Object Detection Using Pre-trained Models
  1. What Is Object Detection?
  2. A Brief Introduction to Artificial Neural Networks
  3. Downloading the ResNet50 Model
  4. Creating the iOS Project
  5. Summary
2. Chapter 6: Creating an Image Classifier with the Create ML App
  1. Introduction to the Create ML App
  2. Creating the Image Classification Model with the Create ML App
  3. Creating the iOS Project
  4. Summary
3. Chapter 7: Creating a Tabular Classifier with Create ML
  1. Preparing the Dataset for the Create ML App
  2. Creating the Tabular Classification Model with the Create ML App
  3. Creating the iOS Project
  4. Summary
4. Chapter 8: Creating a Decision Tree Classifier
  1. Decision Tree Recap
  2. Examining the Dataset
  3. Creating Training and Test Datasets
  4. Creating the Decision Tree Classification Model with Scikit-learn
  5. Using Core ML Tools to Convert the Scikit-learn Model to the Core ML Format
  6. Creating the iOS Project
  7. Summary
5. Chapter 9: Creating a Logistic Regression Model Using Scikit-learn and Core ML
  1. Examining the Dataset
  2. Creating a Training and Test Dataset
  3. Creating the Logistic Regression Model with Scikit-learn
  4. Using Core ML Tools to Convert the Scikit-learn Model to the Core ML Format
  5. Creating the iOS Project
  6. Summary
6. Chapter 10: Building a Deep Convolutional Neural Network with Keras
  1. Introduction to the Inception Family of Deep Convolutional Neural Networks
  2. A Brief Introduction to Keras
  3. Implementing Inception-v4 with the Keras Functional API
  4. Training the Inception-v4 Model
  5. Exporting the Keras Inception-v4 Model to the Core ML Format
  6. Creating the iOS Project
  7. Summary
Appendix A: Anaconda and Jupyter Notebook Setup
1. Installing the Anaconda Distribution
2. Creating a Conda Python Environment
3. Installing Python Packages
4. Installing Jupyter Notebook
5. Summary
Appendix B: Introduction to NumPy and Pandas
1. NumPy
2. Pandas
3. Summary
Index
End User License Agreement

List of Tables

Chapter 1
1. TABLE 1.1: Type and Range of Data Across 100 Sample Application
Chapter 2
1. TABLE 2.1: Type and Range of Data Across 5,000 Sample Applications
2. TABLE 2.2: Transforming Categorical Features Into Numeric Features
3. TABLE 2.3: Modified Input Features
Chapter 4
1. TABLE 4.1: Pros and Cons of Server-Side and Edge-Based Deployment
Chapter 7
1. TABLE 7.1: Minimum and Maximum Values of the Features of the UCI ML Wine Dataset
Appendix B
1. Table B.1: Commonly Used ndarray Attributes

List of Illustrations

Chapter 1
1. FIGURE 1.1 Supervised learning
2. FIGURE 1.2 Clustering technique used to find patterns in the data
3. FIGURE 1.3 Semisupervised learning
4. FIGURE 1.4 A simple linear regression model
5. FIGURE 1.5 Three potential decision boundaries
6. FIGURE 1.6 Data that cannot be classified using a linear decision boundary i...
7. FIGURE 1.7 Data that cannot be classified using a linear decision boundary i...
8. FIGURE 1.8 Nonlinear decision boundary in two-dimensional space
9. FIGURE 1.9 The sigmoid function
10. FIGURE 1.10 Using the sigmoid function for binary classification
11. FIGURE 1.11 Softmax logistic regression
12. FIGURE 1.12 Decision tree visualization
13. FIGURE 1.13 Structure of an ANN
14. FIGURE 1.14 A simple neural network
Chapter 2
1. FIGURE 2.1 Architecture of a rule-based decision system
2. FIGURE 2.2 A flowchart depicting the decision-making process for a rule-base...
3. FIGURE 2.3 Cross-validation using four folds
4. FIGURE 2.4 Using the sigmoid function for binary classification
5. FIGURE 2.5 A class-wise confusion matrix
6. FIGURE 2.6 ROC curves for two binary classification models
Chapter 3
1. FIGURE 3.1 The head() function displays rows from the beginning of a Pandas ...
2. FIGURE 3.2 The head() function displays truncated data for large dataframes.
3. FIGURE 3.3 Impact of the set_index function on a dataframe
4. FIGURE 3.4 Distribution of values for the survived attribute
5. FIGURE 3.5 Histogram of numeric features
6. FIGURE 3.6 Histogram of numeric feature Age using different widths (2, 3, 5,...
7. FIGURE 3.7 Histogram of categorical feature, Embarked
8. FIGURE 3.8 Box plot of numeric features
9. FIGURE 3.9 Box plot of the Age feature variable
10. FIGURE 3.10 Dataframe with engineered feature AgeCategory
11. FIGURE 3.11 Dataframe with engineered feature FareCategory
12. FIGURE 3.12 Histogram of Age, NormalizedAge, and StandardizedAge
13. FIGURE 3.13 Linear correlation between numeric columns
14. FIGURE 3.14 Matrix of scatter plots between pairs of numeric attributes
15. FIGURE 3.15 Variance of data along the x- and y-axes
16. FIGURE 3.16 Projecting two-dimensional data onto a one-dimensional line
17. FIGURE 3.17 Features after principal component analysis
Chapter 5
1. FIGURE 5.1 Object detection techniques applied in industrial inspection
2. FIGURE 5.2 Object detection stages
3. FIGURE 5.3 Structure of an artificial neural network
4. FIGURE 5.4 A simple neural network
5. FIGURE 5.5 Architecture of a convolutional neural network
6. FIGURE 5.6 The convolution operation
7. FIGURE 5.7 The result of successive convolutions
8. FIGURE 5.8 The effect of max pooling
9. FIGURE 5.9 A visualization of CNN layers by Adam Harley
10. FIGURE 5.10 Architecture of VGG16
11. FIGURE 5.11 A network that uses residual learning
12. FIGURE 5.12 Pre-trained Core ML models
13. FIGURE 5.13 Downloading the pre-trained Resnet50 Core ML model
14. FIGURE 5.14 Creating a new iOS project using the Single View App template
15. FIGURE 5.15 Application storyboard with default view controller scene
16. FIGURE 5.16 Using the assistant editor to create outlets
17. FIGURE 5.17 Editing the application's info.plist file
18. FIGURE 5.18 Import settings for the Resnet50.mlmodel file
19. FIGURE 5.19 Overview of the Resnet50.mlmodel file
20. FIGURE 5.20 Accessing the Swift interface to the Core ML model file
21. FIGURE 5.21 A section of the Resnet50.Swift file
22. FIGURE 5.22 VNCoreMLRequest scale and crop options
23. FIGURE 5.23 Results of running the app with the picture of a flowerpot
Chapter 6
1. FIGURE 6.1 Launching the Create ML app
2. FIGURE 6.2 Selecting the Image Classifier template
3. FIGURE 6.3 Create ML project options dialog
4. FIGURE 6.4 Specifying the input dataset
5. FIGURE 6.5 Training data augmentation options
6. FIGURE 6.6 Beginning the model training process
7. FIGURE 6.7 Model performance statistics
8. FIGURE 6.8 Navigating to the test dataset
9. FIGURE 6.9 Predictions on the test dataset
10. FIGURE 6.10 Creating a new iOS project using the Single View App template
11. FIGURE 6.11 Application storyboard with default view controller scene
12. FIGURE 6.12 Using the Assistant Editor to create outlets
13. FIGURE 6.13 Editing the application's Info.plist file
14. FIGURE 6.14 Import settings for the DogsCatsTransferLearningClassifier.mlmode...
15. FIGURE 6.15 Overview of the DogsCatsTransferLearningClassifier.mlmodel file
16. FIGURE 6.16 Accessing the Swift interface to the Core ML model file
17. FIGURE 6.17 A section of the DogsCatsTransferLearningClassifier.Swift file
18. FIGURE 6.18 Results of running the app with the picture of a dog
Chapter 7
1. FIGURE 7.1 Creating a new notebook file
2. FIGURE 7.2 Inspecting the first five rows of the UCI ML wine dataset
3. FIGURE 7.3 Inspecting the first five rows of the UCI ML wine dataset after c...
4. FIGURE 7.4 Inspecting the statistical characteristics of the numeric columns...
5. FIGURE 7.5 Histogram of the data in the target attribute of the Iris dataset
6. FIGURE 7.6 Inspecting the first five rows of the UCI ML wine dataset after s...
7. FIGURE 7.7 Launching the Create ML app
8. FIGURE 7.8 Selecting the Tabular Classifier template
9. FIGURE 7.9 Create ML project options dialog
10. FIGURE 7.10 Specifying the input dataset
11. FIGURE 7.11 Selecting the target attribute
12. FIGURE 7.12 Accessing the feature selection dialog box
13. FIGURE 7.13 Selecting the names of the columns of the dataset that represent...
14. FIGURE 7.14 Specifying the test dataset
15. FIGURE 7.15 Beginning the model training process
16. FIGURE 7.16 Beginning the model training process
17. FIGURE 7.17 Creating a new iOS project using the Single View App template
18. FIGURE 7.18 Using the Attributes Inspector to change the border style of the...
19. FIGURE 7.19 Application storyboard with default view controller scene
20. FIGURE 7.20 Using the assistant editor to create outlets
21. FIGURE 7.21 Setting up the text field delegate
22. FIGURE 7.22 Setting up the selector property of the tap gesture recognizer
23. FIGURE 7.23 Import settings for the WineClassifier.mlmodel file
24. FIGURE 7.24 Overview of the WineClassifier.mlmodel file
25. FIGURE 7.25 Accessing the Swift interface to the Core ML model file
26. FIGURE 7.26 Results of running the app on the iOS simulator
Chapter 8
1. FIGURE 8.1 Creating a new notebook file
2. FIGURE 8.2 Inspecting the first five rows of the Iris flowers dataset
3. FIGURE 8.3 Histogram of the data in the target attribute of the Iris flowers...
4. FIGURE 8.4 Inspecting the first five rows of the Iris flowers dataset after ...
5. FIGURE 8.5 Inspecting the statistical characteristics of the numeric columns...
6. FIGURE 8.6 Executing the code in all the cells of the Jupyter Notebook
7. FIGURE 8.7 Graphical representation of the decision tree model
8. FIGURE 8.8 Creating a new iOS project using the Single View App template
9. FIGURE 8.9 Using the Attributes Inspector to change the border style of the ...
10. FIGURE 8.10 Application storyboard with default view controller scene
11. FIGURE 8.11 Using the Assistant Editor to create outlets
12. FIGURE 8.12 Setting up the text field delegate
13. FIGURE 8.13 Setting up the selector property of the tap gesture recognizer
14. FIGURE 8.14 Import settings for the iris_dtree.mlmodel file
15. FIGURE 8.15 Overview of the iris_dtree.mlmodel file
16. FIGURE 8.16 Accessing the Swift interface to the Core ML model file
17. FIGURE 8.17 Results of running the app on the iOS simulator
Chapter 9
1. FIGURE 9.1 Creating a new notebook file
2. FIGURE 9.2 Inspecting the first five rows of the diabetes dataset
3. FIGURE 9.3 Inspecting the statistical characteristics of the diabetes datase...
4. FIGURE 9.4 Histograms of the data in each column of the dataset
5. FIGURE 9.5 Histograms of the BMI, Glucose, and BloodPressure features
6. FIGURE 9.6 Distribution of the classes in the target attribute of the origin...
7. FIGURE 9.7 The Sigmoid function
8. FIGURE 9.8 Using the Sigmoid function for binary classification
9. FIGURE 9.9 Creating a new iOS project using the Single View App template
10. FIGURE 9.10 Using the Attributes Inspector to change the border style of the...
11. FIGURE 9.11 Application storyboard with default view controller scene
12. FIGURE 9.12 Using the Assistant Editor to create outlets
13. FIGURE 9.13 Setting up the text field delegate
14. FIGURE 9.14 Setting up the selector property of the tap gesture recognizer
15. FIGURE 9.15 Import settings for the diabetes_logreg.mlmodel file
16. FIGURE 9.16 Overview of the diabetes_logreg.mlmodel file
17. FIGURE 9.17 Accessing the Swift interface to the Core ML model file
18. FIGURE 9.18 Results of running the app on the iOS simulator
Chapter 10
1. FIGURE 10.1 A 3×3 convolution operation tiled across a larger image
2. FIGURE 10.2 An Inception block
3. FIGURE 10.3 Modified Inception block using 1 x 1 convolutions
4. FIGURE 10.4 GoogLeNet (Inception-v1) architecture
5. FIGURE 10.5 Inception-v3 blocks
6. FIGURE 10.6 Inception-v4 network architecture
7. FIGURE 10.7 Inception-A, Inception-B, Inception-C blocks
8. FIGURE 10.8 Structure of the stem of the Inception-v4 network
9. FIGURE 10.9 Reduction-A and Reduction-B blocks
10. FIGURE 10.10 Keras' modular architecture
11. FIGURE 10.11 Creating a new notebook file
12. FIGURE 10.12 Padding options for convolutional layers
13. FIGURE 10.13 A one-dimensional strided convolution operation
14. FIGURE 10.14 Inspecting the first few rows of the dfTrainingData dataframe
15. FIGURE 10.15 A plot of the training history
16. FIGURE 10.16 Creating a new iOS project using the Single View App template
17. FIGURE 10.17 Application storyboard with default view controller scene
18. FIGURE 10.18 Using the Assistant Editor to create outlets
19. FIGURE 10.19 Editing the application's Info.plist file
20. FIGURE 10.20 Import settings for the Inceptionv4-dogscats.mlmodel file
21. FIGURE 10.21 Overview of the Inceptionv4-dogscats.mlmodel file
22. FIGURE 10.22 Accessing the Swift interface to the Core ML model file
23. FIGURE 10.23 A section of the Inceptionv4-dogscats.Swift file
24. FIGURE 10.24 Results of running the app with the picture of a dog
Appendix A
1. FIGURE A.1 Anaconda.com home page
2. FIGURE A.2 Downloading the appropriate version of Anaconda distribution
3. FIGURE A.3 Anaconda distribution installer on macOS X
4. FIGURE A.4 Anaconda installer provides the option to install third-party IDE...
5. FIGURE A.5 Anaconda has been successfully installed.
6. FIGURE A.6 Environment settings in Anaconda Navigator
7. FIGURE A.7 Creating a new Conda Python environment
8. FIGURE A.8 Switching to the IOS_ML_Book Conda environment
9. FIGURE A.9 Displaying all available Python packages
10. FIGURE A.10 Searching for a package
11. FIGURE A.11 Package dependencies dialog box
12. FIGURE A.12 Installing Jupyter Notebook
13. FIGURE A.13 Jupyter Notebook running in a web browser

Published simultaneously in Canada

ISBN: 978-1-119-60287-3

ISBN: 978-1-119-60291-0 (ebk)

ISBN: 978-1-119-60290-3 (ebk)

Manufactured in the United States of America

No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: The publisher and the author make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation warranties of fitness for a particular purpose. No warranty may be created or extended by sales or promotional materials. The advice and strategies contained herein may not be suitable for every situation. This work is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If professional assistance is required, the services of a competent professional person should be sought. Neither the publisher nor the author shall be liable for damages arising herefrom. The fact that an organization or website is referred to in this work as a citation and/or a potential source of further information does not mean that the author or the publisher endorses the information the organization or website may provide or recommendations it may make. Further, readers should be aware that Internet websites listed in this work may have changed or disappeared between when this work was written and when it is read.

For general information on our other products and services please contact our Customer Care Department within the United States at (877) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002.

Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.

Library of Congress Control Number: 2020930131

Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates, in the United States and other countries, and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.

Introduction

Machine learning is one of the hottest trends in computing and deals with the problem of creating computer programs that can generalize and predict information reliably, quickly, and with accuracy, resembling what a human would do with similar information. With the recent hype in mainstream media around novel applications of machine learning, you may be inclined to think that machine learning is a relatively new discipline, but that is far from the truth. In fact, machine learning has been around for several decades, and it is because of recent advances in storage, processor, and GPU technology that it is possible to build and deploy machine learning systems at scale and get results in real time.

This book is targeted at intermediate/advanced iOS developers who are looking to come to grips with the fundamentals of machine learning, learn about some of the common tools used by data scientists, and learn how to build and deploy models into their iOS applications. This book at all times attempts to balance theory and practice, giving you enough visibility into the underlying concepts while providing you with the best practices and practical advice that you can apply to your workplace right away.

Machine learning is a rapidly evolving field. I have made every attempt to keep the content up-to-date and relevant. Even though this makes the book susceptible to being outdated on a few rare instances, I am confident the content will remain useful and relevant through the next releases of iOS.

What Does This Book Cover?

This book covers the fundamental concepts of machine learning as well as the use of these concepts to build real-world models and use them in your iOS apps.

Chapter 1: Introduction to Machine Learning This chapter introduces the different types of machine learning models commonly found in real-world applications as well as tools and libraries used by data scientists to build these models. The chapter also includes examples of real-world applications of machine learning and sources of training data.
Chapter 2: The Machine-Learning Approach This chapter examines a hypothetical scenario in which a rule-based system is used to process credit card applications. The limitations of the rule-based system are examined, and a machine learning system is devised to address some of those limitations. The chapter concludes with an overview of the steps involved in building a typical machine learning solution.
Chapter 3: Data Exploration and Preprocessing This chapter focuses on the data exploration and feature engineering stage, specifically the use of popular Python libraries NumPy, Pandas, and Scikit-learn for tabular data. The chapter also explores feature selection techniques.
Chapter 4: Implementing Machine Learning on Mobile Apps This chapter explores the options available to you as an iOS developer to integrate machine learning techniques on your apps. The chapter compares the pros and cons of an edge-based versus server-based deployment model and introduces both Apple offerings as well as other third-party offerings that can be used from within your apps.
Chapter 5: Object Detection Using Pre-trained Models This chapter focuses on the use of pre-trained models for object detection in your iOS apps. The chapter also covers the basics of artificial neural networks (ANNs) and convolutional neural networks (CNNs).
Chapter 6: Creating an Image Classifier with the Create ML App This chapter covers the use of Apple's Create ML app to train a machine learning model that can detect the dominant object in an image. The model is trained on a subset of the Kaggle Dogs vs. Cats dataset and exported to the Core ML format. The exported model is used within an iOS app.
Chapter 7: Creating a Tabular Classifier with Create ML This chapter covers the use of Apple's Create ML app to train a classification model on tabular data. The model is trained on the popular UCI ML wine dataset and exported to the Core ML format using the Create ML app. The trained model is then used in an iOS app that allows users to input the chemical characteristics of wine and learn the quality of the beverage.
Chapter 8: Creating a Decision Tree Classifier This chapter focuses on the use of Scikit-learn to create a decision tree classification model on the popular Iris flowers dataset. The trained model is then exported to the Core ML format using the Core ML Tools Python library and used in an iOS app.
Chapter 9: Create a Logistic Regression Model Using Scikit-learn and Core ML This chapter focuses on the use of Scikit-learn to create a logistic regression model on the popular Pima Indians diabetes dataset. The trained model is then exported to the Core ML format using the Core ML Tools Python library and used in an iOS app.
Chapter 10: Building a Deep Convolutional Neural Network with Keras This chapter covers the creation and training of a popular deep convolutional neural network architecture called Inception V4 using the Keras functional API. The Inception V4 network is trained on a small publicly available dataset and then used in an iOS app.
Appendix A: Anaconda and Jupyter Notebook Setup This appendix helps you install Anaconda Navigator on your computer, set up a Python environment that includes several common machine learning libraries, and configure Jupyter Notebook.
Appendix B: Introduction to NumPy and Pandas This appendix shows you how to use NumPy and Pandas. These libraries are commonly used during the data exploration and feature engineering phases of a project.

Additional Resources

In addition to this book, here are some other resources that can help you learn more about machine learning.

Apple Machine Learning Journal: https://machinelearning.apple.com
Scikit-learn User Guide: https://scikit-learn.org/stable/user_guide.html
Core ML Developer Documentation: https://developer.apple.com/documentation/coreml
Core ML Tools Documentation: https://apple.github.io/coremltools/
Keras Documentation: https://keras.io

Reader Support for This Book

We provide support for this book in a couple of ways.

Companion Download Files

As you work through the examples in this book, the project files you need are all available for download from www.wiley.com/go/machinelearningforiosdevelopers.

How to Contact the Publisher

If you believe you've found a mistake in this book, please bring it to our attention. At John Wiley & Sons, we understand how important it is to provide our customers with accurate content, but even with our best efforts an error may occur.

To submit your possible errata, please email it to our customer service team at wileysupport@wiley.com with the subject line “Possible Book Errata Submission.”

Machine Learning for iOS Developers

About the Author

About the Technical Editor

Acknowledgments