John Wiley & Sons Machine Learning for iOS Developers Cover Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniq.. Product #: 978-1-119-60287-3 Regular price: $43.71 $43.71 In Stock

Machine Learning for iOS Developers

Mishra, Abhishek

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1. Edition April 2020
Softcover
Wiley & Sons Ltd

ISBN: 978-1-119-60287-3
John Wiley & Sons

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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.

Introduction xix

Part 1 Fundamentals of Machine Learning 1

Chapter 1 Introduction to Machine Learning 3

Chapter 2 The Machine-Learning Approach 29

Chapter 3 Data Exploration and Preprocessing 47

Chapter 4 Implementing Machine Learning on Mobile Apps 73

Part 2 Machine Learning with CoreML, CreateML, and TuriCreate 81

Chapter 5 Object Detection Using Pre- trained Models 83

Chapter 6 Creating an Image Classifier with the Create ML App 111

Chapter 7 Creating a Tabular Classifier with Create ML L 135

Chapter 8 Creating a Decision Tree Classifier 175

Chapter 9 Creating a Logistic Regression Model Using Scikit-learn and Core ML 203

Chapter 10 Building a Deep Convolutional Neural Network with Keras 235

Appendix A Anaconda and Jupyter Notebook Setup 287

Appendix B Introduction to NumPy and Pandas 297

Index 315
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.