Autonomous Cars: Deep Learning and Computer Vision in Python90% OFF Discount Coupon

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

4.8 out of 5
12,768 students
Created by Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education Team
English
Updated November 2025

Quick Facts — Course Summary

Here's a quick overview of everything you need to know about Autonomous Cars: Deep Learning and Computer Vision in Python before you enroll:

Course Name: Autonomous Cars: Deep Learning and Computer Vision in Python
Platform: Udemy
Instructor: Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education Team
Coupon Last Verified: November 13, 2025
Level: All Levels
Topic: Development
Subtopic: Computer Vision
Total Time: 12h 30m of video content
Language: English
Access Type: Unlimited lifetime access + updates
Certificate: Included upon completion from Udemy
Main Skills: Automatically detect lane markings in images · Detect cars and pedestrians using a trained classifier and with SVM · Classify traffic signs using Convolutional Neural Networks
Requirements: Windows, Mac, or Linux PC with at least 3GB free disk space. · Some prior experience in programming.
Current Price: $9.99 (was $99.99). You save $90.00 with 90% discount.
How to Apply: Click the coupon button to activate your discount automatically
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Skills You'll Master

By the end of Autonomous Cars: Deep Learning and Computer Vision in Python, you'll have these practical skills:

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.

What You Need Before Starting

Before enrolling in Autonomous Cars: Deep Learning and Computer Vision in Python, make sure you have:

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

About This Udemy Course

The following is the full official course description for Autonomous Cars: Deep Learning and Computer Vision in Python as published on Udemy by instructor Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education Team:

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!

Compare Similar Courses

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Is the Autonomous Cars: Deep Learning and Computer Vision in Python Coupon Worth It?

Expert review by Andrew Derek, Lead Course Analyst at CoursesWyn.Last updated: November 13, 2025.

Based on analysis of the curriculum structure, student engagement metrics, and verified rating data, Autonomous Cars: Deep Learning and Computer Vision in Python is a high-value resource for learners seeking to build skills inDevelopment. Taught by Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education Team on Udemy, the 12h 30m course provides a structured progression from foundational concepts to advanced techniques— making it suitable for learners at all levels. The current coupon reduces the price by 90%, from $99.99 to $9.99, removing the primary financial barrier to enrollment.

What We Like (Pros)

  • Verified 90% price reduction makes this course accessible to learners on any budget.
  • Aggregate student rating of 4.8 out of 5 indicates high learner satisfaction.
  • Strong enrollment base with over 12,768 students demonstrates course popularity and trust.
  • Includes an official Udemy completion certificate and lifetime access to all future content updates.

!Keep in Mind (Cons)

The following limitations should be considered before enrolling in Autonomous Cars: Deep Learning and Computer Vision in Python:

  • The depth of Development coverage may be challenging for absolute beginners without the listed prerequisites.
  • Lifetime access is contingent on the continued operation of the Udemy platform.
  • Hands-on projects and quizzes require additional time investment beyond video watch time.
Final Verdict: Worth It
This course offers exceptional value with current pricing

Course Rating Summary

Autonomous Cars: Deep Learning and Computer Vision in Python Course holds an aggregate rating of 4.8 out of 5 based on 12,768 student reviews on Udemy.

4.8
★★★★★
12,768 Verified Ratings
5 stars
75%
4 stars
15%
3 stars
6%
2 stars
2%
1 star
2%

* Rating distribution is approximated from the aggregate score. Sourced from Udemy.

Instructor Profile

The following section provides background information on Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education Team, the instructor responsible for creating and maintaining Autonomous Cars: Deep Learning and Computer Vision in Python on Udemy.

Autonomous Cars: Deep Learning and Computer Vision in Python is taught by Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education Team, a Udemy instructor specializing in Development. For the full instructor biography, professional credentials, and a complete list of their courses, visit the official instructor profile on Udemy.

Instructor Name: Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education Team
Subject Area: Development
Teaching Approach: Practical, project-based instruction focused on real-world application of Development skills.

Frequently Asked Questions

The following questions and answers cover the most common queries about Autonomous Cars: Deep Learning and Computer Vision in Python, its coupon code, pricing, and enrollment process.

About the Author

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Andrew Derek

Lead Course Analyst at CoursesWyn with 8+ years of experience evaluating online learning platforms. I've analyzed 500+ Udemy courses and helped thousands of learners choose the right courses for their career goals.

4.8/5 Rating
Trusted by 10K+ Students

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