Computer Vision
A complete 25-lesson Computer Vision course — from pixels, classical filtering, and features through CNN architectures, transfer learning, object detection, segmentation, vision transformers, and generative vision models, culminating in an image classifier, a fine-tuned classifier, and a simple object detector.
25 Lessons · 750 min · Learning Platform Editorial Team
What you need on the desk
- Python 3 with PyTorch and torchvision installed (CPU-only is fine for every example in this course)
- A code editor (VS Code recommended)
- No GPU or paid cloud account required — every example runs on CPU, just slower
Know this much before you start
- Comfort with Python and PyTorch fundamentals — this course assumes you already know tensors, autograd, nn.Module, training loops, and basic CNNs (the Deep Learning with PyTorch course on this site covers all of this)
- This course does not re-teach general deep learning mechanics — it goes deep specifically into vision: image representation, classical image processing, and vision-specific architectures and tasks
- No prior computer vision or OpenCV experience needed
By the end, you can
- Explain how images are represented as tensors and apply classical filtering and edge detection
- Build, train, and evaluate CNN-based image classifiers, and apply transfer learning to new vision tasks
- Explain object detection and semantic segmentation fundamentals, including IoU and mAP-style evaluation
- Explain modern vision architectures: ResNet's skip connections, Vision Transformers, and generative vision models
- Choose the right architecture and evaluation metric for a given vision task
- Build and test real small computer vision projects — an image classifier, a fine-tuned classifier, and a simple object detector