Description
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.
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.
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