Data Privacy and Security in Machine Learning

As machine learning models process increasingly sensitive personal data, ensuring privacy and security has become a critical requirement for developers and data professionals. This text-based course guides you through the essential concepts of safeguarding data throughout the machine learning lifecycle. You will transition from understanding basic privacy principles to implementing robust data protection strategies. By reading through conceptual breakdowns and analyzing code-based examples, you will learn how to defend models against privacy attacks and apply modern anonymization techniques. What you'll learn: Understand foundational privacy concepts, regulatory standards, and common security threats in machine learning; Apply data anonymization and de-identification techniques to prepare datasets safely; Implement differential privacy to mathematically guarantee individual data protection during model training; Explore federated learning concepts to train machine learning models across decentralized data sources; Identify and mitigate vulnerabilities such as membership inference and model inversion attacks. The course begins with core definitions and theoretical frameworks before moving into practical implementation strategies. You will progress through structured text lessons and written scenarios designed to build your confidence in designing secure machine learning pipelines. This course is designed for beginner data scientists, software engineers, and privacy enthusiasts who want to build secure systems. No advanced security or machine learning background is required. Start reading today to build trust and security into your machine learning workflows.