Computer Vision Masterclass

Learn in practice everything you need to know about Computer Vision! Build projects step by step using Python!

Created by: Jones Granatyr
Professor

Created by: SuperDataScience Team
Helping Data Scientists Succeed

Created by: Ligency Team
Helping Data Scientists Succeed

Created by: Gabriel Alves
Developer

Created by: AI Expert Academy
Instructor

What Will I Learn?

• Understand the basic intuition about Cascade and HOG classifiers to detect faces
• Implement face detection using OpenCV and Dlib library
• Learn how to detect other objects using OpenCV, such as cars, clocks, eyes, and full body of people
• Compare the results of three face detectors: Haarcascade, HOG (Histogram of Oriented Gradients) and CNN (Convolutional Neural Networks)
• Detect faces using images and the webcam
• Understand the basic intuition about LBPH algorithm to recognize faces
• Implement face recognition using OpenCV and Dlib library
• Recognize faces using images and the webcam
• Understand the basic intuition about KCF and CSRT algorithms to perform object tracking
• Learn how to track objects in videos using OpenCV library
• Learn everything you need to know about the theory behind neural networks, such as: perceptron, activation functions, weight update, backpropagation, gradient descent and a lot more
• Implement dense neural networks to classify images
• Learn how to extract pixels and features from images in order to build neural networks
• Learn the theory behind convolutional neural networks and implement them using Python and TensorFlow
• Implement transfer learning and fine tuning to get incredible results when classifying images
• Use convolutional neural networks to classify the following emotions in images and videos: happy, anger, disgust, fear, surprise and neutral
• Compress images using linear and convolutional autoencoders
• Detect objects in images in videos using YOLO, one of the most powerful algorithms today
• Recognize gestures and actions in videos using OpenCV
• Learn how to create hallucinogenic images with Deep Dream
• Learn how to revive famous artists with style transfer
• Create images that don't exist in the real world with GANs (Generative Adversarial Networks)
• Implement image segmentation do extract useful information from images and videos

Requirements

• Programming logic
• Basic Python programming

Target audience

• Beginners who are starting to learn Computer Vision
• Undergraduate students who are studying subjects related to Artificial Intelligence
• People who want to solve their own problems using Computer Vision
• Students who want to work in companies developing Computer Vision projects
• People who want to know all areas inside Computer Vision, as well as know the problems that these techniques are able to solve
• Anyone interested in Artificial Intelligence or Computer Vision
• Data scientists who want to grow their portfolio
• Professionals who want to understand how to apply Computer Vision to real projects