Introduction to Computer Vision Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This 8-week course provides a comprehensive introduction to computer vision, combining foundational theory with hands-on practice using OpenCV, PyTorch, and TensorFlow. Learners will spend approximately 6-8 hours per week implementing image processing techniques, extracting features, building deep learning models, and developing real-world applications. The course concludes with an end-to-end project, solidifying skills in object recognition, classification, and deployment. Lifetime access ensures flexible, self-paced learning.
Module 1: Image Fundamentals
Estimated time: 12 hours
- Digital image representation and pixel operations
- Color spaces (RGB, grayscale, HSV)
- Basic image processing with OpenCV
- Image arithmetic and geometric transformations
Module 2: Feature Extraction
Estimated time: 12 hours
- Edge detection using Sobel and Canny filters
- Corner detection with Harris algorithm
- SIFT feature detection and description
- Feature matching and image stitching applications
Module 3: Deep Learning for Vision
Estimated time: 12 hours
- Introduction to Convolutional Neural Networks (CNNs)
- Architectures for image classification
- Transfer learning with pre-trained models
- Data augmentation techniques
Module 4: Application Development
Estimated time: 12 hours
- Face detection using Haar cascades and deep learning
- Optical Character Recognition (OCR) pipelines
- Medical imaging analysis applications
Module 5: Real-World Applications and Deployment
Estimated time: 10 hours
- Building end-to-end computer vision systems
- Model optimization for inference
- Evaluating performance in production environments
Module 6: Final Project
Estimated time: 20 hours
- Design and implement an image classification pipeline
- Apply feature extraction and deep learning techniques
- Submit working code notebook with documentation
Prerequisites
- Proficiency in Python programming
- Familiarity with basic machine learning concepts
- Access to GPU recommended for deep learning tasks
What You'll Be Able to Do After
- Process and manipulate digital images using OpenCV
- Extract and match key features from images
- Build and train CNNs for image classification
- Apply computer vision techniques to real-world problems
- Develop and deploy end-to-end vision applications