Machine Learning for iOS Developers

HARNESS THE POWER OF APPLE IOS MACHINE LEARNING (ML) CAPABILITIES AND LEARN THE CONCEPTS AND TECHNIQUES NECESSARY TO BE A SUCCESSFUL APPLE IOS MACHINE LEARNING PRACTITIONER!

Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple’s ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications.

Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book’s clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models—both pre-trained and user-built—with Apple’s CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers:

* Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics
* Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming
* Develop skills in data acquisition and modeling, classification, and regression.
* Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS)
* Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreML

Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps.

ABHISHEK MISHRA has more than 19 years of experience across a broad range of mobile and enterprise technologies. He consults as a security and fraud solution architect with Lloyds Banking group PLC in London. He is the author of Machine Learning on the AWS Cloud, Amazon Web Services for Mobile Developers, iOS Code Testing, and Swift iOS: 24-Hour Trainer. Introduction xix

PART 1 FUNDAMENTALS OF MACHINE LEARNING 1

CHAPTER 1 INTRODUCTION TO MACHINE LEARNING 3

What is Machine Learning? 4

Tools Commonly Used by Data Scientists 4

Common Terminology 5

Real-World Applications of Machine Learning 7

Types of Machine Learning Systems 8

Supervised Learning 9

Unsupervised Learning 10

Semisupervised Learning 11

Reinforcement Learning 11

Batch Learning 12

Incremental Learning 12

Instance-Based Learning 13

Model-Based Learning 13

Common Machine Learning Algorithms 13

Linear Regression 14

Support Vector Machines 15

Logistic Regression 19

Decision Trees 21

Artificial Neural Networks 23

Sources of Machine Learning Datasets 24

Scikit-learn Datasets 24

AWS Public Datasets 27

Kaggle.com Datasets 27

UCI Machine Learning Repository 27

Summary 28

CHAPTER 2 THE MACHINE-LEARNING APPROACH 29

The Traditional Rule-Based Approach 29

A Machine-Learning System 33

Picking Input Features 34

Preparing the Training and Test Set 39

Picking a Machine-Learning Algorithm 40

Evaluating Model Performance 41

The Machine-Learning Process 44

Data Collection and Preprocessing 44

Preparation of Training, Test, and Validation Datasets 44

Model Building 45

Model Evaluation 45

Model Tuning 45

Model Deployment 46

Summary 46

CHAPTER 3 DATA EXPLORATION AND PREPROCESSING 47

Data Preprocessing Techniques 47

Obtaining an Overview of the Data 47

Handling Missing Values 57

Creating New Features 60

Transforming Numeric Features 62

One-Hot Encoding Categorical Features 64

Selecting Training Features 65

Correlation 65

Principal Component Analysis 68

Recursive Feature Elimination 70

Summary 71

CHAPTER 4 IMPLEMENTING MACHINE LEARNING ON MOBILE APPS 73

Device-Based vs Server-Based Approaches 73

Apple’s Machine Learning Frameworks and Tools 75

Task-Level Frameworks 75

Model-Level Frameworks 76

Format Converters 76

Transfer Learning Tools 77

Third-Party Machine-Learning Frameworks and Tools 78

Summary 79

PART 2 MACHINE LEARNING WITH COREML, CREATEML, AND TURICREATE 81

CHAPTER 5 OBJECT DETECTION USING PRE- TRAINED MODELS 83

What is Object Detection? 83

A Brief Introduction to Artificial Neural Networks 86

Downloading the ResNet50 Model 92

Creating the iOS Project 92

Creating the User Interface 95

Updating Privacy Settings 100

Using the Resnet50 Model in the iOS Project 100

Summary 109

CHAPTER 6 CREATING AN IMAGE CLASSIFIER WITH THE CREATE ML APP 111

Introduction to the Create ML App 112

Creating the Image Classification Model with the Create ML App 113

Creating the iOS Project 117

Creating the User Interface 118

Updating Privacy Settings 122

Using the Core ML Model in the iOS Project 123

Summary 132

CHAPTER 7 CREATING A TABULAR CLASSIFIER WITH CREATE ML 135

Preparing the Dataset for the Create ML App 135

Creating the Tabular Classification Model with the Create ML App 143

Creating the iOS Project 147

Creating the User Interface 148

Using the Classification Model in the iOS Project 156

Testing the App 172

Summary 173

CHAPTER 8 CREATING A DECISION TREE CLASSIFIER R 175

Decision Tree Recap 175

Examining the Dataset 176

Creating Training and Test Datasets 180

Creating the Decision Tree Classification Model with Scikit-learn 181

Using Core ML Tools to Convert the Scikit-learn Model to the Core ML Format 186

Creating the iOS Project 187

Creating the User Interface 188

Using the Scikit-learn Decision Tree Classifier Model in the iOS Project 193

Testing the App 201

Summary 202

CHAPTER 9 CREATING A LOGISTIC REGRESSION MODEL USING SCIKIT-LEARN AND CORE ML 203

Examining the Dataset 203

Creating a Training and Test Dataset 208

Creating the Logistic Regression Model with Scikit-learn 210

Using Core ML Tools to Convert the Scikit-learn Model to the Core ML Format 216

Creating the iOS Project 218

Creating the User Interface 219

Using the Scikit-learn Model in the iOS Project 225

Testing the App 232

Summary 233

CHAPTER 10 BUILDING A DEEP CONVOLUTIONAL NEURAL NETWORK WITH KERAS 235

Introduction to the Inception Family of Deep Convolutional Neural Networks 236

GoogLeNet (aka Inception-v1) 236

Inception-v2 and Inception-v3 238

Inception-v4 and Inception-ResNet 239

A Brief Introduction to Keras 244

Implementing Inception-v4 with the Keras Functional API 246

Training the Inception-v4 Model 259

Exporting the Keras Inception-v4 Model to the Core ML Format 269

Creating the iOS Project 270

Creating the User Interface 271

Updating Privacy Settings 276

Using the Inception-v4 Model in the iOS Project 277

Summary 286

APPENDIX A ANACONDA AND JUPYTER NOTEBOOK SETUP 287

Installing the Anaconda Distribution 287

Creating a Conda Python Environment 288

Installing Python Packages 291

Installing Jupyter Notebook 293

Summary 296

APPENDIX B INTRODUCTION TO NUMPY AND PANDAS 297

NumPy 297

Creating NumPy Arrays 297

Modifying Arrays 301

Indexing and Slicing 304

Pandas 305

Creating Series and Dataframes 305

Getting Dataframe Information 307

Selecting Data 311

Summary 313

Index 315