Machine Learning for iOS Developers

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John Wiley & Sons, Mar 4, 2020 - Computers - 336 pages

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.

 

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Contents

The MachineLearning Approach
29
The MachineLearning Process
44
Data Exploration and Preprocessing
47
Selecting Training Features
65
Summary
71
ThirdParty MachineLearning Frameworks and Tools
78
Machine Learning with CoreML CreateML and TuriCreate
81
Summary
109
Using Core MLTools to Convert the Scikitlearn Model to the Core ML Format
186
Summary
202
Using Core MLTools to Convert the Scikitlearn Model to the Core ML Format
216
Summary
233
Implementing Inceptionv4 with the Keras Functional API
246
Training the Inceptionv4 Model
259
Appendix A Anaconda and Jupyter Notebook Setup
287
Installing Jupyter Notebook
293

Creating the iOS Project
117
Summary
132
Creating the iOS Project
147
Summary
173
Pandas
305
Summary
313
Index
315
Copyright

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About the author (2020)

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.

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