Autonomous Cars: Deep Learning and Computer Vision in Python (Udemy.com)
Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars
Created by: Sundog Education by Frank Kane
Last updated July 2026
What you will 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
How it compares
Against the other 126 rated Udemy courses in Deep Learning on CourseDuck:
- Rating4.5 out of 5higher than 46% of them
- Reviews1,543#41 of 127 by number of reviews
- Length12.5 hourslonger than 59% of them
- Last updatedJuly 2026more recently than 88% of them
Course Description
Autonomous Cars: Computer Vision and Deep Learning
The automotive industry is experiencing a paradigm shift from conventional, human-driven vehicles into self-driving, artificial intelligence-powered vehicles. Self-driving vehicles offer a safe, efficient, and cost effective solution that will dramatically redefine the future of human mobility. Self-driving cars are expected to save over half a million lives and generate enormous economic opportunities in excess of $1 trillion dollars by 2035. The automotive industry is on a billion-dollar quest to deploy the most technologically advanced vehicles on the road.
As the world advances towards a driverless future, the need for experienced engineers and researchers in this emerging new field has never been more crucial.
The purpose of this course is to provide students with knowledge of key aspects of design and development of self-driving vehicles. The course provides students with practical experience in various self-driving vehicles concepts such as machine learning and computer vision. Concepts such as lane detection, traffic sign classification, vehicle/object detection, artificial intelligence, and deep learning will be presented. The course is targeted towards students wanting to gain a fundamental understanding of self-driving vehicles control. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this self-driving car course will master driverless car technologies that are going to reshape the future of transportation.
Tools and algorithms we'll cover include:
OpenCV
Deep Learning and Artificial Neural Networks
Convolutional Neural Networks
Template matching
HOG feature extraction
SIFT, SURF, FAST, and ORB
Tensorflow and Keras
Linear regression and logistic regression
Decision Trees
Support Vector Machines
Naive Bayes
Your instructors are Dr. Ryan Ahmed with a PhD in engineering focusing on electric vehicle control systems, and Frank Kane, who spent 9 years at Amazon specializing in machine learning. Together, Frank and Dr. Ahmed have taught over 500,000 students around the world on Udemy alone.
Students of our popular course, "Data Science, Deep Learning, and Machine Learning with Python" may find some of the topics to be a review of what was covered there, seen through the lens of self-driving cars. But, most of the course focuses on topics we've never covered before, specific to computer vision techniques used in autonomous vehicles. There are plenty of new, valuable skills to be learned here!
Instructor Details
- 4.5 Rating
1,543 Reviews
Sundog Education by Frank Kane
Sundog Education's mission is to make highly valuable career skills in data engineering, data science, generative AI, AWS, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford.
As a senior manager at Amazon during its early years, Frank Kane led large technical teams, managed multi-year projects, and served as a "bar-raiser" - interviewing thousands and hiring hundreds across the company.
Frank holds 26 issued patents in machine learning and personalization. After 9 years at Amazon and IMDb, he founded Sundog Education and has since taught over 1.1 million people in AI, ML, AWS, system design, and tech leadership. Frank has been teaching full-time for over 10 years - but is always building projects on the side with the latest emerging technologies, to better explain them to you!
Our teaching style is to simplify complex concepts - once you cut through all the notation and jargon, most of AI and machine learning is pretty easy to understand. We make these advanced topics accessible to anyone with a modest technical background, always using plain English and examples to drive it home. We make learning easy, hands-on, and fun.
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Reviews
By Yasin Beyazit on 3/2/2023
The instructor is very personable and emphasizes practical skills over theory. This approach keeps the lessons engaging and relevant to real-world applications, and ensures that the course doesn't feel tedious. Overall, the instruction is geared towards preparing students for success in the practical aspects of their field.
By Sorn Hovanotayan on 1/20/2022
Thank you for your corse. I am confident that this course can help everyone understand a deep learning for autonomous driving better. And if you also offer another course dedicated for object detection using cnn, r-cnn, fast r-cnn and YOLO, that would be perfect lol
By Pablo Javier González on 1/10/2022
Great overview of many methods and technics used in image processing, machine learning and specifically in computer vision applications. The lessons were really clear and focused in practical tasks, with different aproches to many real problems in the area of autonomous driving cars. The only thing I would add to this course is a bit more of the mathematical background, but that's just my personal likes. Other than that, I think it's a great choice for anyone looking for a intuitive and practical approach to this subject.
By Kenneth V Allsford on 5/1/2021
The course was a nice refresher (previously I had completed Andrew Ng's course related to use of AI for image recognition on Coursera). However there is significant overlap and/or discontinuity from the 2 presenters including within the same module: (i) this resulted in loss od module summary; (ii) if this was a book it would have been very frustrating but is just about ok for video presentation. Final comment is that in reality this is much more of a course on image recognition rather than self-driving cars i.e. it doesn't get into the practicalities of dealing with the uncertainties that exist in the recognition approaches discussed herein.
By Rakesh Sadhu on 10/22/2020
looks good so far
By Shannon Russell on 10/13/2020
Excellent! Clear descriptions of how cars and people see!
By Zamal Ali on 10/12/2020
I expected a better interpretation of this course but their way of teaching turned out to be ambiguous.
By Edward Larson on 9/7/2020
Yes. It was very enjoyable and rewarding.
By Andinet Negash Hunde on 9/6/2020
Awful and poor.
By Mike Buchanan on 8/21/2020
Frank provides great concise explanations of material. But the sections covered by Dr. Ryan were just okay - the demonstrations were good, but I don't think he's great at teaching.
By Kushagra Shrivastava on 8/19/2020
so many issues with the software no proper instructions
By Satish Kumar Reddy on 8/11/2020
It is a really good course for beginners to Deep Learning and Autonomous Vehicles. Concepts are very well explained. Ryan is very descriptive but a little slow at times, but puts his point across very effectively.
Quality Score
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Overall Score : 90 / 100












