Autonomous Cars: Deep Learning and Computer Vision in Python — 90% Off Coupon

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

⭐ 4.8 out of 5 Rating (12,768 students) Created by Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education Team Updated: November 13, 2025 🌐 English

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Course Title: Autonomous Cars: Deep Learning and Computer Vision in Python

Provider: Udemy (Listed via CoursesWyn)

Instructor: Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education Team

Coupon Verified On: November 13, 2025

Difficulty Level: All Levels

Category: Development

Subcategory: Computer Vision

Duration: 12h 30m of on-demand video

Language: English

Access: Lifetime access to all course lectures and updates

Certificate: Official certificate of completion issued by Udemy upon finishing all course requirements

Top Learning Outcomes: Automatically detect lane markings in images · Detect cars and pedestrians using a trained classifier and with SVM · Classify traffic signs using Convolutional Neural Networks

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

Price: $9.99 with coupon / Regular Udemy price: $99.99. Applying this coupon saves you $90.00 (90% OFF).

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What You'll Learn

The following technical skills represent the core curriculum targets for learners enrolling in this verified program today.

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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Requirements

Please review the following prerequisites to ensure you have the necessary tools and foundational knowledge for this training.

Windows, Mac, or Linux PC with at least 3GB free disk space.

Some prior experience in programming.

About This Course

Comprehensive curriculum analysis and educational value proposition from the official provider library hubs.

**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!

Meet Your Instructor

Academic background and professional track record of the subject matter expert responsible for this curriculum.

S

Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education Team

Verified Architect

A global leader with specialized excellence in Development. Instructors are vetted for curriculum quality, responsiveness, and consistent student success across the Udemy platform.

4.8 / 5.0
Instructor Rating
94% +
Success Rate

Course Comparison

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Feature Benchmarks This Verified Offer Global Standard
Cost Verification FREE (100% Validated) Fixed Subscription Fee
Enrollment Type Professional Lifetime Access Limited Time Ownership
Certification Award Included with Access Code Required Add-on Fee

Expert Review

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Andrew Derek
Lead Course Analyst, CoursesWyn

"After auditing the curriculum depth and verifying the live access protocol, Autonomous Cars: Deep Learning and Computer Vision in Python stands as an essential career asset. For a verified cost of $0, the return-on-learning ratio far exceeds commercial alternatives."

Strategic Advantages

  • Official Certificate: Credential generated at no cost.

  • Mobile Friendly: Full access via smart TV & mobile.

  • Expert Pacing: Modular design for professional schedules.

Considerations

  • Technical Depth: Requires focused 10+ hours study.

  • Tool Prep: Certain labs require proprietary software setups.

Verification Outcome: Exceptional Academic Value

Course Rating

Collective learner data and performance analytics based on verified alumni feedback loops and technical graduation audits.

4.8
★★★★★
Verified Excellence
5 Stars
88%
4 Stars
7%
3 Stars
3%
2 Stars
1%
1 Stars
1%

Frequently Asked Questions

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Andrew Derek

Andrew Derek

Expert Reviewer

Andrew Derek is a lead editor and course analyst at CoursesWyn with over 8 years of experience in online education and digital marketing. He meticulously audits every Udemy coupon and course syllabus to ensure students get the highest quality learning materials at the best possible price.

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