Autonomous Cars: Deep Learning and Computer Vision in Python

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

4.52 (1388 reviews)

Udemy

platform

English

language

Software Engineering

What you will 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

Course Gallery

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Autonomous Cars: Deep Learning and Computer Vision in Python – Screenshot 4 — Screenshot 4 – Autonomous Cars: Deep Learning and Computer Vision in Python

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Comidoc Review

Our Verdict

Though providing an excellent foundation in image processing, machine learning, and computer vision, the course falls short of its promise to delve into the practical aspects of autonomous vehicles. While engaging and well-structured, content updates and a stronger focus on real-world applications are necessary to meet advertised expectations.

What We Liked

Covers a wide range of topics in image processing, machine learning, and computer vision, providing a solid foundation for more specialized studies.
Excellent presentation of course materials with clear video lessons and programming exercises. Instructor explanations are clear and easy to follow.
Emphasizes practical skills over theory, keeping lessons engaging and relevant to real-world applications.
Comprehensive material preparation with numerous examples, raw data downloads, and well-defined codes.

Potential Drawbacks

Course title is misleading; content does not directly address the practical aspects of self-driving cars as expected.
May lack in-depth mathematical background explanations for some topics, which could be a drawback for those interested in theoretical foundations.
Some course materials are outdated due to regular API updates.
Insufficient focus on autonomous car concepts; many examples have no relation to self-driving cars.