Dive into a comprehensive 3.5-hour course on computer vision, exploring machine learning fundamentals, neural networks, and advanced architectures. Learn about supervised and unsupervised learning, data processing techniques, and model evaluation. Discover key computer vision applications and image representation methods. Explore essential neural network components, including neurons, activation functions, and convolutions. Examine popular architectures like LeNet, AlexNet, VGG, and ResNet. Delve into object detection techniques, including bounding boxes, anchor boxes, and models such as R-CNN and YOLO. Investigate semantic segmentation and fully convolutional networks, culminating with the U-Net architecture. Gain hands-on experience using Jupyter Notebooks on SageMaker to reinforce your learning.