What the course covers.
- Module 1
Image Processing & Fundamentals
Learners begin by understanding core computer vision concepts, including image manipulation, filtering, and feature extraction. This module covers Python libraries such as OpenCV and PIL, helping participants preprocess images and prepare datasets for AI model training. Hands-on exercises focus on real-world tasks such as edge detection, transformations, and basic visual recognition workflows.
- Module 2
Object Detection & Recognition
This module dives into building AI models for object detection and image classification. Learners work with convolutional neural networks (CNNs) and modern architectures such as YOLO and Vision Transformers. Practical exercises involve training models on sample datasets, detecting objects in images and videos, and understanding how to optimise models for accuracy and speed.
- Module 3
Advanced Applications & Deployment
Learners apply their knowledge to design and deploy complete computer vision systems. Topics include model evaluation, performance tuning, real-time video processing, and deployment strategies for cloud or edge devices. Participants also explore specialised applications such as facial recognition, medical imaging, and satellite image analysis.
