Learn Computer Vision and Deep Learning for Self-Driving Cars

Sundog Education's latest course is out! Frank Kane and co-instructor Dr. Ryan Ahmed have teamed up to teach you the technology behind self-driving cars. You'll learn deep learning and computer vision using OpenCV, Tensorflow, and Python.

You'll get over 12 hours of content, with lots of hands-on activities and several practice projects. Along the way, you'll learn about:

Using OpenCV for transforming and convolving images and video streams
Edge detection using Canny, Sobel, and Laplace algorithms
Corner detection with the Harris algorithm
Identifying lane markings in a video stream
HOG, SIFT, SURF, FAST, and ORB feature extraction with OpenCV
Machine learning fundamentals: linear and logistic regression, decision trees, Naive Bayes, and SVM
Artificial neural networks and multi-layer perceptrons
Deep learning with Tensorflow and Keras
Convolutional Neural Networks for feature classification with deep learning

I've teamed up with Dr. Ryan Ahmed, whose PhD in engineering focuses on control systems and AI. As an expert in computer vision, he'll present the computer vision portions of the course, while I'll jump in to teach you machine learning and deep learning. If you've taken Sundog Education's course on Data Science, Machine Learning, and Deep Learning, you'll find some of the course to be a review, with a new spin on how machine learning applies to self-driving cars. Computer vision however is a topic we've never touched on before.