Autonomous Cars: Deep Learning and Computer Vision in Python

Description

In this course, you will :

• 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.