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Autonomous Cars: Deep Learning and Computer Vision in Python

Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars
4.8 ★★★★★12,768 studentsCreated by Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education TeamLast updated Nov 13, 2025🌐 English

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

Requirements

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

Description

Frequently Asked Questions

Student Feedback

4.8
★★★★★
Course Rating
75%
15%
5%
5%
5%
S
Sarah J.
★★★★★2 weeks ago

This course was absolutely amazing! The instructor explained everything clearly and the projects were very helpful.

M
Michael T.
★★★★1 month ago

Great content, highly recommended for beginners. Just wish there were more practice exercises.

D
David K.
★★★★★2 months ago

Best course on this topic I've taken so far. Worth every penny (even better since I got it for free!).

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This course includes:
  • 📺 12h 30m on-demand video
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  • ♾️ Full lifetime access
  • 🏆 Certificate of completion