Autonomous Cars: Deep Learning and Computer Vision in Python

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!

This Udemy coupon unlocks a guided path into Autonomous Cars: Deep Learning and Computer Vision in Python, so you know exactly what outcomes to expect before you even press play.

Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education Team leads this Udemy course in Development, blending real project wins with step-by-step coaching.

The modules are sequenced to unpack Computer Vision step by step, blending theory with scenarios you can reuse at work while keeping the Udemy course reviews tone in mind.

Video walkthroughs sit alongside quick-reference sheets, checklists, and practice prompts that make it easy to translate the material into real projects, especially when you grab Udemy discounts like this one.

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Sundog Education by Frank Kane, Frank Kane, Prof. Ryan Ahmed, Mitchell Bouchard, Sundog Education Team also keeps an eye on the Q&A and steps in quickly when you need clarity. You'll find fellow learners trading tips, keeping you motivated as you sharpen your Development skill set with trusted Udemy discounts.

Ready to dive into Autonomous Cars: Deep Learning and Computer Vision in Python? This deal keeps the momentum high and hands you the tools to apply Computer Vision with confidence while your Udemy coupon is still active.