Learners will apply machine learning models, design intuitive interfaces, and implement real-time image and text classification to build intelligent iOS applications. By completing this course, learners will gain hands-on experience integrating Core ML 3 and Vision into practical workflows, enabling them to develop apps that recognize images, classify text, and interact seamlessly with device cameras.
This course provides a guided, end-to-end learning path—from setting up an Xcode project and managing permissions to processing images, handling orientation metadata, and executing predictions using pre-trained and custom models. Learners also explore creating their own text classification datasets and integrating model outputs into user-friendly UI components.
What makes this course unique is its practical, project-based approach, using real Core ML models, real device capabilities, and real examples that mirror professional iOS development scenarios. Instead of focusing on theory alone, learners build functioning ML-powered features step by step, gaining confidence to deploy intelligent apps on Apple platforms. By the end, they will have a complete workflow for adding machine learning to any iOS app.
This module introduces learners to Core ML 3, essential iOS development concepts, and the foundational tools required to build intelligent mobile apps. Learners explore the project structure, understand Core ML workflows, set up an Xcode project, manage permissions, and work with the camera and photo library to acquire images for machine learning processing.
What's included
9 videos4 assignments
Show info about module content
9 videos•Total 75 minutes
Introduction to Project•10 minutes
New Features Core ML 3•7 minutes
Control Flow•6 minutes
Creat New Xcode Project•11 minutes
Action Sheet•9 minutes
How to Fetch Image•8 minutes
Photo Library and Camera•9 minutes
Adding Camera Usage Description•5 minutes
Difference Between UIImage•10 minutes
4 assignments•Total 60 minutes
Getting Started with Core ML 3•10 minutes
Starting the iOS Project•10 minutes
Working with Images in iOS•10 minutes
Foundations of Core ML 3 & iOS App Setup•30 minutes
Building Intelligence: Image Classification Workflow
Module 2•2 hours to complete
Module details
This module guides learners through the complete image classification pipeline using Core ML and the Vision framework. They integrate pre-trained models, design user interfaces for classification apps, process images correctly, manage orientation issues, and execute predictions using real-time camera or photo library inputs.
What's included
9 videos4 assignments
Show info about module content
9 videos•Total 73 minutes
Using VN Image Request Handler •8 minutes
SqueezeNet ML Model•6 minutes
Creating Model•12 minutes
Designing UI•9 minutes
Designing UI Continue•7 minutes
Creating IBOutlets•4 minutes
Open Camera•9 minutes
Identify Image using Custom Image•10 minutes
Identify Image using Custom Image Continue•7 minutes
4 assignments•Total 60 minutes
Image Processing & Model Integration•10 minutes
UI Development for Image Classification•10 minutes
Capturing & Classifying Images•10 minutes
Building Intelligence: Image Classification Workflow•30 minutes
Advanced ML Models: Text Classification in Core ML 3
Module 3•2 hours to complete
Module details
This module explores text classification using Core ML 3, covering dataset preparation, category definition, model integration, and user input processing. Learners will complete the full pipeline to classify text inputs, interpret model outputs, and deliver meaningful results through an iOS app interface.
What's included
5 videos3 assignments
Show info about module content
5 videos•Total 45 minutes
Custom Text Classifier Model•9 minutes
Custom Text Classifier Model Continue•9 minutes
Text Classifier Model Part 1•8 minutes
Text Classifier Model Part 2•12 minutes
Text Classifier Model Part 3•8 minutes
3 assignments•Total 50 minutes
Creating a Custom Text Classifier•10 minutes
Text Classification Deep Dive•10 minutes
Advanced ML Models: Text Classification in Core ML 3•30 minutes
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