Machine Learning for iOS Developers - Softcover

Mishra, Abhishek

Softcover

ISBN 10: 1119602874 ISBN 13: 9781119602873

Publisher: Wiley, 2020

Synopsis

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.

About this title

About the Author

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.

From the Back Cover

Learn how to harness machine learning in your iOS apps!

Machine Learning (ML) is a discipline within Artificial Intelligence (AI) that enables computers to learn patterns from data and use these patterns to categorize and predict information reliably and quickly without being explicitly programmed. The Apple iOS Software Development Kit (SDK) includes the Core ML framework that allows developers to integrate ML services into both mobile and desktop applications. Assuming no prior experience in the field, Machine Learning for iOS Developers covers building machine learning models with Apple's new Create ML app, as well as building models using popular Python libraries such as NumPy, Pandas, Scikit-learn and Keras. The book then teaches you how to integrate these models in iOS applications using Apple's Core ML framework and the Core ML tools library. Step-by-step guidance, hands-on activities, real-world scenarios, and downloadable source code examples make Machine Learning for iOS Developers a must-have resource for anyone wishing to learn the concepts and techniques needed to be a successful Apple iOS Machine Learning practitioner.

This practical, clearly-written guide shows how to:

• Understand ML data collection, preprocessing, and feature engineering.
• Learn fundamental machine learning algorithms.
• Create machine learning models with the new CreateML App.
• Create machine learning models with Scikit-learn and CoreML tools.
• Integrate Core ML models in iOS Applications.
• Use pre-trained machine learning models.
• Create a deep learning network from scratch with Keras and use it in an iOS app.
• Create decision trees and random forest models.

From the Inside Flap

Learn how to harness machine learning in your iOS apps!

Machine Learning (ML) is a discipline within Artificial Intelligence (AI) that enables computers to learn patterns from data and use these patterns to categorize and predict information reliably and quickly without being explicitly programmed. The Apple iOS Software Development Kit (SDK) includes the Core ML framework that allows developers to integrate ML services into both mobile and desktop applications. Assuming no prior experience in the field, Machine Learning for iOS Developers covers building machine learning models with Apple's new Create ML app, as well as building models using popular Python libraries such as NumPy, Pandas, Scikit-learn and Keras. The book then teaches you how to integrate these models in iOS applications using Apple's Core ML framework and the Core ML tools library. Step-by-step guidance, hands-on activities, real-world scenarios, and downloadable source code examples make Machine Learning for iOS Developers a must-have resource for anyone wishing to learn the concepts and techniques needed to be a successful Apple iOS Machine Learning practitioner.

This practical, clearly-written guide shows how to:

• Understand ML data collection, preprocessing, and feature engineering.
• Learn fundamental machine learning algorithms.
• Create machine learning models with the new CreateML App.
• Create machine learning models with Scikit-learn and CoreML tools.
• Integrate Core ML models in iOS Applications.
• Use pre-trained machine learning models.
• Create a deep learning network from scratch with Keras and use it in an iOS app.
• Create decision trees and random forest models.

Edition details

• Publisher: Wiley
• Publication date: 2020
• Language: English
• ISBN 10: 1119602874
• ISBN 13: 9781119602873
• Binding: Paperback
• Edition number: 1
• Number of pages: 336