Course Overview
- Course Title: Certified Computer Vision & Image Processing
- Instructor: Muhammad Shafiq (Data Scientist | AI & ML Engineer | Lecturer | Researcher)
- Target Audience:
- Aspiring AI/ML engineers
- Computer vision enthusiasts
- Software developers transitioning to CV
- Researchers in image processing
- Students pursuing AI, robotics, or automation
- Prerequisites:
- Basic Python programming knowledge
- Familiarity with linear algebra and calculus (recommended)
Curriculum Highlights
- Key Topics Covered:
- Fundamentals of image processing (filtering, transformations, morphology)
- Feature detection & matching (SIFT, SURF, ORB, Harris corners)
- Object detection & tracking (Haar cascades, HOG, background subtraction)
- Deep learning for CV (CNNs, YOLO, Faster R-CNN, segmentation models)
- OpenCV library (image/video I/O, drawing, contour analysis)
- Real-world applications (OCR, facial recognition, medical imaging)
- Model optimization & deployment (quantization, ONNX, TensorRT)
- Key Skills Learned:
- Implementing image preprocessing pipelines
- Building custom object detectors using deep learning
- Applying feature extraction for pattern recognition
- Developing real-time video processing systems
- Optimizing CV models for edge devices
- Deploying scalable computer vision solutions
Course Format
- Duration: ~10 hours of on-demand video
- Format: Self-paced online course with lifetime access
- Resources:
- 3 practice tests (certification prep)
- Downloadable code templates (Jupyter Notebooks, Python scripts)
- Mobile & TV access
- Certificate of completion