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    Autonomous Cars: Deep Learning And Computer Vision In Python

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    Autonomous Cars: Deep Learning And Computer Vision In Python - Online Course Download
    Autonomous Cars: Deep Learning And Computer Vision In Python - Online Course Download
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    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

    Requirements :

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

    Description :

    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 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.

    Course Size Details :

    13 hours on-demand video
    1 article
    Full lifetime access
    Access on mobile and TV
    Certificate of completion

    View Demo

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