This project will develop an open access, web-based user interface for machine learning tools currently in use as desktop applications by UNC Health Sciences Library (HSL) and create an accompanying full-day curriculum to be delivered to research librarians at multiple institutions at no cost. This funding will allow UNC HSL to share their expertise with other institutions around applying these innovative approaches to analyzing bibliographic data. Machine learning approaches in use at UNC HSL include desktop applications with publicly available algorithms (e.g., K-means, nonnegative matrix factorization, latent dirichlet allocation, Naïve Bayes) used for clustering, semi-supervised learning, and machine learning. Other applications of these tools include the ability to analyze collections, impact, and other library-specific data.