This Computer Vision From Scratch course provides a complete introduction to modern computer vision and deep learning concepts with practical explanations and hands-on learning. Designed by an MIT PhD instructor, the course focuses on building strong foundations in visual AI systems and neural networks.

The course begins with the basics of computer vision and image processing before introducing filters and convolution operations, which are fundamental to convolutional neural networks (CNNs). Learners then explore how neural networks process visual information for image classification tasks.

As the course progresses, students learn about training deep learning models, hyperparameter tuning, and experiment tracking using machine learning tools. Important optimization techniques such as regularization, dropout, batch normalization, and early stopping are also covered to improve model performance and reduce overfitting.

The course introduces transfer learning, pretrained models, and fine-tuning strategies commonly used in real-world AI projects. Learners also study famous CNN architectures including AlexNet, VGGNet, GoogleNet (Inception), SqueezeNet, and ResNet, understanding how each model transformed the field of computer vision.

Through practical examples and theoretical explanations, the course helps learners build a deep understanding of image classification and modern deep learning techniques used in AI-powered visual systems.

By the end of the course, learners will have strong foundations in computer vision, CNNs, and deep learning model development.