Free Computer Vision

Comprehensive deep learning specialization by Andrew Ng. Master neural networks, CNNs, RNNs, and modern deep learning architectures.

Master convolutional neural networks and modern computer vision architectures for image classification and object detection.

Computer Vision with PyTorch

For those with an intermediate to advanced understanding of computer vision, this course covers advanced topics like deep learning, convolutional neural networks (CNNs), object detection, image segmentation, and generative models. It is taught by a renowned expert in the field and is designed for students with a strong programming background.

This course reviews the mechanisms behind anthropogenic climate change and its impact on global temperatures and weather patterns. It includes two case studies: one using time series analysis for wind power forecasting and another using computer vision for biodiversity monitoring, demonstrating how AI techniques can help mitigate and adapt to climate change.

This beginner-level course introduces the exciting field of Computer Vision and its applications in various industries. You will learn about computer vision, its applications, and how to process images using Python, Watson AI, and OpenCV. The course also covers building image classification models and custom classifiers.

A guided project that teaches the fundamentals of using TensorFlow for image classification. You will build, train, and evaluate a neural network to classify images from the CIFAR-10 dataset.

This professional certificate teaches how to build and train deep learning models using PyTorch. It covers applying transfer learning and fine-tuning to pretrained models for computer vision and natural language processing.

This course from Harvard University introduces the field of Tiny Machine Learning (TinyML), which involves running machine learning models on low-power microcontrollers. It covers the fundamentals of deep learning, data collection, and model deployment on embedded devices, with a focus on applications like keyword spotting and image classification.

An introductory course to computer vision that covers image processing and the practical application of the OpenCV library with Python for AI and Machine Learning tasks. It provides insights into various methods for working with images.

Computer Vision with Deep Learning

OpenCV Python Tutorial 1 - Introduction & Images

What is OpenCV? - Python Beginners Tutorial 1

OpenCV Python Tutorial For Beginners 24 - Motion Detection and Tracking Using OpenCV Contours

Stanford University course on deep learning for computer vision. Learn to implement, train and debug CNNs and gain understanding of cutting-edge research.

Learn TensorFlow 2.0 and Keras for deep learning. Build neural networks for computer vision, NLP, and time series prediction.

Complete PyTorch tutorial from basics to advanced topics. Learn tensors, autograd, neural networks, CNNs, RNNs, and more.

Learn the fundamentals of deep learning including neural networks, CNNs, RNNs, and hands-on with TensorFlow and Keras.