Computer Vision

A comprehensive curriculum to learning Computer Vision
Master Computer Vision in 50 Lessons: A Comprehensive Learning Curriculum

Master Computer Vision in 50 Lessons: A Comprehensive Learning Curriculum

Computer vision is a rapidly growing field that enables machines to extract useful information from digital images and videos. With applications ranging from self-driving cars to facial recognition, computer vision has become an essential skill for data scientists and engineers. In this blog post, we present a comprehensive learning curriculum consisting of 50 lessons designed to help you master computer vision from scratch. Each lesson link below is a tutorial providing you with step-by-step learning to turn you into a Computer Vision professional

The Curriculum:

  1. Introduction to Computer Vision
  2. Image Representation and Manipulation
  3. Color Spaces
  4. Image Histograms
  5. Image Filtering
  6. Edge Detection
  7. Image Thresholding
  8. Morphological Transformations
  9. Image Segmentation
  10. Feature Detection and Description
  11. Feature Matching
  12. Image Stitching
  13. Face Detection
  14. Object Detection
  15. Image Classification
  16. Convolutional Neural Networks (CNNs)
  17. Transfer Learning
  18. Semantic Segmentation
  19. Instance Segmentation
  20. Optical Character Recognition (OCR)
  21. Optical Flow
  22. Video Processing
  23. Background Subtraction
  24. Object Tracking
  25. Deep Learning for Object Tracking
  26. Pose Estimation
  27. Action Recognition
  28. Image-to-Image Translation
  29. Generative Adversarial Networks (GANs)
  30. Style Transfer
  31. Image Super-Resolution
  32. Image Compression
  33. Depth Estimation
  34. 3D Reconstruction
  35. Camera Calibration
  36. Augmented Reality
  37. Image Synthesis
  38. Generative Adversarial Networks (GANs) for Image Synthesis
  39. Facial Recognition
  40. Emotion Recognition
  41. Age and Gender Prediction
  42. Image Captioning
  43. Visual Question Answering
  44. Scene Understanding
  45. Active Learning for Computer Vision
  46. Data Augmentation
  47. Unsupervised Learning for Computer Vision
  48. Reinforcement Learning for Computer Vision
  49. Few-Shot Learning for Computer Vision
  50. Evaluating Computer Vision Models