[Video tutorial] Train a Linear Classifier on Encrypted Data Using Concrete ML and Fully Homomorphic Encryption (FHE)

Concrete ML is a suite of privacy-preserving machine learning tools designed to simplify the integration of Fully Homomorphic Encryption (FHE) for developers, enabling the automatic conversion of machine learning models into their homomorphic equivalents. In a video tutorial, Luis Montero, a machine learning engineer at Zama, demonstrates Concrete ML's latest feature, FHE training. The initiative encourages users to engage with the project by starring the Concrete ML GitHub repository, exploring its documentation, seeking support through community channels, and participating in the Zama Bounty Program & Grant Program to further advance the FHE space.