This one-hour live webinar will introduce participants to the fundamentals of Privacy Preserving Machine Learning (PPML). The session will introduce key PPML concepts such as Federated Learning, Differential Privacy, and Homomorphic Encryption, giving participants a foundational understanding of how to balance privacy and transparency with the effectiveness of ML models. During the webinar, attendees will get practical insights into integrating privacy-preserving techniques into ML workflows using PySyft - an open source tool for secure and private machine learning. Registration: https://openmined.github.io/intro-to-ppml-workshop