Autonomous Cars: Deep Learning and Computer Vision in Python

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

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 12h 14m | 3.17 GB
eLearning | Skill level: All Levels

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

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 the design and development of self-driving vehicles. The course is for students who want 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.

  • The tools and algorithms we’ll cover include:
  • OpenCV, Deep Learning, and Artificial Neural Networks
  • Convolutional Neural Networks
  • Template matching and HOG feature extraction
  • Tensorflow and Keras
  • Linear regression and logistic regression
  • Decision Trees, Support Vector Machines, and Naive Bayes

Your instructors are Dr. Ryan Ahmed, who has a Ph.D. in engineering focusing on electric vehicle control systems, and Frank Kane, who spent nine years at Amazon specializing in machine learning. Together, Frank and Dr. Ahmed have taught over 200,000 students around the world.

The course provides students with practical experience in various self-driving vehicle 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.


  • 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
  • Analyse 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