Applying the Hugging Face Machine Learning Pipelines in Python

Hugging Face is a community-driven initiative that develops and promotes artificial intelligence tools for a wide range of applications. The organization provides pretrained, state-of-the-art deep learning models that can be easily applied to real-world machine learning tasks. In this course, you’ll explore the Hugging Face library with a focus on practical applications in natural language processing (NLP) and computer vision. You’ll begin by understanding how transformer-based models are used within the Hugging Face ecosystem. You’ll gain hands-on experience applying the Hugging Face pipeline API to common NLP tasks, such as text classification, summarization, question answering, and sentiment analysis. Next, you’ll explore computer vision pipelines, including image classification, object detection, and image segmentation. By the end of this course, you’ll be comfortable using a wide range of Hugging Face pipelines for common machine learning tasks and implementing them in Python using PyTorch.