Autonomous Cars: Deep Learning and Computer Vision in Python

Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars

Created by: Sundog Education by Frank Kane
Join over 900K students learning ML, AI, AWS, and Data Eng.

Created by: Frank Kane
Ex-Amazon Sr. Engineer and Sr. Manager, CEO Sundog Education

Created by: Dr. Ryan Ahmed, Ph.D., MBA
Best-Selling Professor, 400K+ students, 250K+ YT Subs

Created by: Mitchell Bouchard
B.S, Host @RedCapeLearning 600,000 + Students

Created by: Sundog Education Team
Sundog Education Team

What Will I Learn?

• Automatically detect lane markings in images
• Detect cars and pedestrians using a trained classifier and with SVM
• Classify traffic signs using Convolutional Neural Networks
• Identify other vehicles in images using template matching
• Build deep neural networks with Tensorflow and Keras
• Analyze and visualize data with Numpy, Pandas, Matplotlib, and Seaborn
• Process image data using OpenCV
• Calibrate cameras in Python, correcting for distortion
• Sharpen and blur images with convolution
• Detect edges in images with Sobel, Laplace, and Canny
• Transform images through translation, rotation, resizing, and perspective transform
• Extract image features with HOG
• Detect object corners with Harris
• Classify data with machine learning techniques including regression, decision trees, Naive Bayes, and SVM
• Classify data with artificial neural networks and deep learning

Requirements

• Windows, Mac, or Linux PC with at least 3GB free disk space.
• Some prior experience in programming.

Target audience

• Software engineers interested in learning the algorithms that power self-driving cars.