Explore the concept of differential privacy and its application in machine learning through this 27-minute talk from EuroPython 2020. Learn how to integrate diffprivlib with scikit-learn and numpy to train accurate models with robust privacy guarantees. Discover the importance of data privacy in today's world and how to protect trained models from privacy vulnerabilities. Gain insights into mechanisms, models, tools, and budget accounting in privacy-preserving machine learning. Follow along with practical examples of running classifiers, building baselines, and creating histograms while maintaining data privacy. No prior knowledge of data privacy or differential privacy is required, but a basic understanding of scikit-learn is expected.