Online Courses Udemy - 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, Frank Kane, Dr. Ryan Ahmed, Ph.D., MBA, Mitchell Bouchard
- Last updated 4/2019
- English
- English [Auto-generated]
- 13 hours on-demand video
- 1 article
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
What you'll 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
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