Machine Learning and Swift: Building Smart Apps for iOS/macOS

The book "Machine Learning and Swift: Building Smart Apps for iOS/macOS" is a practical guide for developers who want to integrate artificial intelligence into their applications for the Apple ecosystem. The author, Alexander Vetrov, an experienced iOS developer and Data Science specialist, systematically guides the reader from the basics of machine learning to creating full-fledged products using Apple frameworks.

What this book is about

The book covers the complete cycle of working with machine learning in Swift:

• ML Basics in Swift — the mathematical minimum needed to understand models, and implementing simple algorithms in pure Swift.
• Core ML — integrating ready-made models (including models from TensorFlow and PyTorch) into applications, working with on-device predictions.
• Create ML — training your own models directly on a Mac: image classification, text analysis, regression, recommendations.
• Vision and Natural Language — computer vision (face detection, object recognition, OCR) and natural language processing (tokenization, sentiment analysis, speech recognition).
• Sound Analysis — audio classification and sound event detection.
• On-device vs Cloud ML — when to use local models and when to use server-side solutions (including working with ML Services from Apple).
• Performance — model optimization, metal shaders, reducing app size, memory management.
• Practical Projects — an app for emotion recognition from photos, a smart translator, a recommendation system for podcasts.

Who this book is for

• iOS/macOS developers who want to add AI features to their applications.
• Data Scientists who want to learn how to deploy models on Apple devices.
• Students and beginner programmers familiar with the basics of Swift.
• Product managers who want to understand the technical capabilities of ML on the Apple platform.

What you will learn

• How to convert models from Python (TensorFlow, PyTorch, scikit-learn) into the Core ML format.
• How to train models without Data Science experience using Create ML.
• How to use pre-trained models from Apple for object, face, and text recognition.
• How to process natural language: sentiment analysis, entity extraction, machine translation.
• How to test and debug models on the simulator and real devices.
• How to publish applications with ML features on the App Store without violating privacy policies.

The book contains over 50 code examples, ready-made Xcode projects, and checklists for ML implementation. All examples are compatible with the latest versions of Swift, Xcode, and iOS/macOS.

This is the ideal guide for those who want to keep up with the times and create truly "smart" applications that are fast, secure, and work offline.