Explore practical approaches to privacy-preserving machine learning in Python during this 19-minute PyCon US talk by Catherine Nelson. Discover tools and techniques for building accurate machine learning models while safeguarding user privacy, including federated learning and algorithms for training on encrypted data. Learn about the landscape of Python solutions for privacy-preserving ML, their integration into machine learning pipelines, and the trade-offs associated with each method. Gain insights into the ethical considerations of using personal data for ML model training, and explore packages such as TensorFlow Privacy, TensorFlow Encrypted, and PySyft. Understand concepts like differential privacy, encrypted models, and federated learning, along with their appropriate use cases and limitations. Equip yourself with practical knowledge to navigate the complex intersection of machine learning and data privacy in today's tech landscape.