AI in Computer Vision for Images, Video, and Real-World Applications (Level 3)

Course Overview

SUMMARY:

Computer Vision for Images and Video: Methods, Applications, and Real-World Challenges equips participants with practical skills to turn image and video data into actionable insights using AI. The course covers foundational image processing, classical computer vision techniques, and deep learning methods such as CNNs for image classification, object detection, and basic segmentation. Participants will also learn video analysis, motion tracking, and strategies to handle real-world challenges like lighting, occlusion, and data limitations.

Through hands-on projects and case studies, participants gain experience designing, training, and evaluating vision models for applications such as defect detection, safety monitoring, counting, and tracking. By the end of the course, participants are prepared to prototype and implement computer vision solutions across manufacturing, healthcare, smart transportation, security, and other sectors.

This course is specifically designed to provide participants with:

• The ability to understand how image and video data can be transformed into meaningful insights for better decision-making
• Practical skills to preprocess and analyze images and video using standard Python tools and libraries
• Hands-on experience in designing and training simple CNN-based models for image classification and basic detection tasks
• Knowledge to evaluate and improve the robustness of computer vision systems under real-world conditions, including variations in lighting, noise, and motion
• The capability to plan, prototype, and apply computer vision solutions to address organizational challenges such as quality inspection, safety monitoring, and process optimization

Course Curriculum

Learning Outcomes

Upon completion of this course, you will be able to:

• Understand fundamentals of digital images, pixels, resolution, and color spaces
• Apply image enhancement and filtering techniques for preprocessing
• Use classical computer vision techniques for edge detection and feature extraction
• Design and train CNN-based models for image classification
• Implement transfer learning and fine-tune pre-trained models
• Build object detection and basic segmentation models
• Analyze video data and implement motion tracking
• Handle real-world challenges like lighting variations, noise, and occlusion
• Deploy computer vision models on servers and edge devices
• Apply computer vision solutions to organizational challenges in manufacturing, healthcare, security, and more

Technologies and Libraries You'll Learn

• Python - Core programming language
• OpenCV - Computer vision and image processing library
• NumPy - Numerical computing for image arrays
• PIL/Pillow - Image processing and manipulation
• TensorFlow/Keras - Deep learning frameworks for CNNs
• PyTorch - Alternative deep learning framework
• Matplotlib - Visualization of images and results
• Jupyter Notebook - Interactive development environment

Who Should Take This Course?

• Data scientists and ML engineers wanting to specialize in computer vision
• Software developers interested in building vision-based AI applications
• Professionals working in manufacturing, quality control, or automation
• Researchers and engineers in healthcare, security, or smart transportation
• Anyone wanting to understand how to extract insights from image and video data

Key Benefits

• Comprehensive Coverage - From image fundamentals to advanced CNN and video analysis
• Real-World Applications - Learn to solve practical problems like defect detection, safety monitoring, and tracking
• Hands-On Projects - Build and deploy computer vision models through practical exercises
• Industry-Relevant Skills - Techniques applicable across manufacturing, healthcare, security, and transportation sectors
• Deployment Knowledge - Understand how to deploy vision models on servers and edge devices
• Problem-Solving Focus - Learn to handle real-world challenges like lighting, noise, and data limitations

Prerequisites

This course is designed for participants with prior knowledge of Python programming and basic machine learning concepts. Familiarity with NumPy, Pandas, and basic deep learning (neural networks) is recommended. Experience with data preprocessing and model training would be helpful. The course builds upon foundational ML knowledge and focuses specifically on computer vision applications.

Certification

Upon successful completion of this course, you will receive a certificate from MASET (Malaysian Association of Skills Training). This certificate validates your practical skills in computer vision and AI applications, demonstrating your ability to build CNN models, analyze images and video, handle real-world vision challenges, and deploy computer vision solutions.

The course includes hands-on projects and case studies to ensure you have practical experience with computer vision pipelines, preparing you for advanced roles in AI vision applications across various industries.