Autonomous Cars: Deep Learning and Computer Vision in Python
Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars
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
- Windows, Mac, or Linux PC with at least 3GB free disk space.
- Some prior experience in programming.
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:
- 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 200,000 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!
Who this course is for:
- Software engineers interested in learning the algorithms that power self-driving cars.
Created by Sundog Education by Frank Kane, Frank Kane, Dr. Ryan Ahmed, Ph.D., MBA, Mitchell Bouchard
Last updated 5/2020
Size: 7.36 GB