Description
Three Pillars of Modern Computer Vision: Detection, Recognition & Generation
This course bundles three cutting-edge computer vision techniques into one powerful learning journey: face detection with OpenCV, object detection with SSD, and image creation with GANs. You’ll build a complete AI vision pipeline—from identifying objects to generating new ones.
Projects You’ll Build
- Happiness detector—classify facial emotions in real time
- Epic horse detector—find and label galloping horses in Monument Valley
- GAN-based image generator—create new faces, art, or synthetic data
- Face detection pipeline using Haar cascades and deep learning
What You’ll Learn
- Face detection intuition—Haar cascades vs deep learning approaches
- SSD (Single Shot Detector)—fast, accurate object detection for real-world use
- Generative Adversarial Networks (GANs)—how generators and discriminators compete
- Hands-on coding in Python with OpenCV, TensorFlow, and Keras
Why These Three Together?
Modern vision systems often combine these techniques:
- Detect faces → Recognize emotions → Generate avatars
- Find objects → Classify them → Augment with AR
This course teaches you the full stack—not just isolated concepts.
Who Is This For?
- Intermediate Python developers exploring AI
- Students working on vision-based final-year projects
- Data scientists expanding into generative models
- AI hobbyists building creative apps
From Detection to Creation
You’ll graduate with not just one, but three portfolio-ready projects that demonstrate your mastery of computer vision’s most in-demand skills.
Don’t just analyze images—understand, detect, and create them. Enroll now.
