Keep learning AI

Intended for learners who have a basic foundation in Python and AI (those that have completed the Practicum AI Beginner Series), the Practicum AI Intermediate series has six modules. These modules each focus on a particular method of AI, introducing learners to hands-on applications of each method and preparing them to apply these methods in their own projects.

Computer Vision Computer vision primarily refers to the field of computer science that focuses on developing techniques that enable computers to understand and interpret visual information from the world. It involves algorithms and systems for acquiring, processing, analyzing, and understanding images and, in some cases, high-dimensional data from the real world (such as hyperspectral imaging or multi-modal models). The goal is to emulate human visual perception, though the applications can be broader. The advancements in deep learning, particularly convolutional neural networks (CNNs) and, more recently, vision transformers (ViTs), have significantly propelled the capabilities in this domain.

Transfer Learning For the vast majority of AI practitioners, there is little need to develop models from scratch! In most cases, there are models that have already been developed and can be reused either directly or with minimal modification for new tasks. Transfer learning is the process of taking a pretrained model and updating it to work in a new situation. Most AI applications involve some aspect of transfer learning. This course will introduce you to the basics of using transfer learning to harness the vast number of pretrained models. Using transfer learning reduces the amount of training data required, speeds up AI application development, and leverages prior work to move your project forward faster.