Embeddings enable a wide range of natural language processing (NLP) and machine learning capabilities by transforming text into a format that machines can understand. In this course, join instructor Kesha Williams as she covers the basics of embeddings and their practical applications in text classification, clustering, semantic search, and more, with hands-on coding examples along the way.

Learn how to use and manage embeddings through the embeddings API, addressing cost and storage aspects as you go. Kesha provides an overview of the complexities and ethics of embeddings, highlighting common inherent biases and the future of AI technologies. By the end of this course, you’ll have a richer understanding of the theoretical knowledge and practical skills required to effectively apply embeddings in a wide range of use case scenarios.