iOS Mobile App. Dev.

HRDC Reg. No: 10001612400
Duration: 2 Days (14 Hours)

Course Overview

This 2-day hands-on course introduces iOS developers to building intelligent mobile apps using Swift, SwiftUI, and Apple’s native AI/ML frameworks. Participants will learn to generate content using Foundation Models, analyze text using Natural Language API, extract text from images using Vision, train custom models with Create ML, and integrate them via Core ML. Prior experience with Swift and Xcode is required.

Who Should Attend

• iOS Developers expanding into AI/ML

• Mobile App Engineers integrating smart features

• Software Developers transitioning to Swift-based development

• Technical Leads and Architects exploring Apple’s ML tools

Why Choose This Course

This HRDC claimable course (Reg. No: 10001612400) provides the essential tools, APIs, and workflows needed to embed artificial intelligence into native iOS apps. Whether you're analyzing user sentiment, translating signs, or building predictive models, you’ll gain real-world skills to power the next generation of intelligent mobile applications.

Learning Outcomes

By the end of the course, participants will be able to:

• Use Apple’s Foundation Models to generate AI-driven text

• Apply sentiment analysis to evaluate user input using Natural Language API

• Recognize and extract text from images using Vision

• Train custom machine learning models using Create ML

• Import and use Core ML models in SwiftUI applications

• Build intelligent iOS apps that adapt and respond to real-world data

Prerequisites

• Completion of Develop in Swift and SwiftUI or equivalent experience

• Proficient in Swift, Xcode, and iOS app design principles

• Familiarity with macOS development environment

Lab Setup / System Requirements

• A Mac running macOS Ventura or Sequoia

• Xcode (latest version) installed

• Stable internet connection for downloads and API calls

Teaching Methodology

• Instructor-led live coding and walkthroughs

• Guided coding labs with real-world app examples

• Conceptual discussions and troubleshooting

• Mini-projects with hands-on integration of AI and ML APIs

• Peer review and collaborative debugging