Develop Intelligent iOS Apps with Swift: Understand Texts, Classify Sentiments, and Autodetect Answers in Text Using NLP

What can an iOS app do when it can make sense of language—not just display it? In Develop Intelligent iOS Apps with Swift, Özgür Sahin walks through practical ways to bring natural language processing and machine learning into Swift projects, with examples that turn concepts into working app ideas.

The book begins with the foundations of machine learning and NLP, then moves into Apple’s tools and hands-on text-processing applications. Its focus is firmly on building: classify messages, generate text, find answers in documents, summarize passages, and connect trained models to iOS software.

From NLP concepts to iOS features

Early chapters introduce machine learning and natural language processing before surveying Apple tools such as Vision and its text-recognition capabilities. The discussion also covers language identification and recognizing people, places, and organizations in text—useful building blocks for apps that need to interpret more than a string of characters.

Build with text classification, generation, and search

Worked examples explore spam classification with Create ML and Turi Create, text generation, and question answering with a BERT model. The book also covers speech recognition and text summarization. Together, these topics show how language-aware features can fit into familiar app experiences, from filtering messages to finding information in a document.

Bring machine-learning models into your Swift workflow

For developers interested in models beyond Apple’s built-in tools, a later chapter demonstrates training a text-classification model in Keras, converting it to Core ML, and testing and using it in Xcode. That progression gives readers a view of the route from model preparation to iOS integration.

Who may find it useful

This book is intended for novice developers and programmers who want to add natural language processing to iOS applications or become familiar with Apple’s native machine-learning tools. Readers interested in Swift, Core ML, Create ML, text analytics, or practical NLP app projects will find a focused starting point.

For developers ready to make text an active part of the user experience, Sahin’s examples offer a clear place to begin. 💻