Machine Learning and Swift: From Idea to Application

The book "Machine Learning and Swift" is a practical guide for developers who want to integrate artificial intelligence technologies into their iOS and macOS applications. You will learn how to use modern Apple frameworks (Core ML, Create ML, Vision, Natural Language) to create intelligent features — from image recognition to text analysis and predictive analytics.

What this book is about

The book covers the complete cycle of working with machine learning on Apple platforms:

• ML Basics in Swift: how neural networks work, supervised and unsupervised learning, quality metrics.
• Core ML: integrating ready-made models (including models from TensorFlow and PyTorch), optimizing performance on devices.
• Create ML: training your own models without deep Data Science knowledge — image classification, text, tabular data.
• Vision and Natural Language: face, object, and emotion recognition; text processing (tokenization, language identification, sentiment analysis).
• Advanced Techniques: on-device training, Transfer Learning, working with Core ML Tools for model conversion.
• Practical Projects: building a plant classifier app, a review analyzer, and a recommendation system.

Who this book is for

This book will be useful for:

• iOS/macOS developers who want to add AI features to their projects.
• Data Scientists looking to understand model deployment on mobile devices.
• Students and beginner programmers familiar with basic Swift and interested in machine learning.

What you will learn

• How to select and prepare data for on-device training.
• How to convert models from Python (Keras, PyTorch) to Core ML format.
• How to test and debug ML models in Xcode.
• How to ensure data privacy (On-Device ML).
• How to publish apps with ML on the App Store.

The book contains numerous code examples, screenshots, and ready-made projects — you can immediately apply the knowledge you gain. The emphasis is on practice: each chapter ends with an assignment for independent work. After studying the material, you will be able to create applications that adapt to the user, recognize content, and make data-driven decisions.