Explore the fundamentals of AI security, adversarial attack types, and essential defense strategies to protect machine learning systems. Gain practical knowledge of poisoning, tampering, and supply chain risks in AI environments.
This course introduces the core concepts of AI and machine learning security, focusing on the landscape of adversarial threats and foundational protection techniques. Learners will examine how AI systems can be compromised through poisoning, tampering, and supply chain attacks, and will discover practical methods to secure models and data pipelines. By the end of the course, participants will understand the key risks facing AI systems and be equipped to implement baseline defenses against common adversarial tactics. Through a combination of clear explanations, real-world examples, and structured quizzes, this course guides learners from foundational AI concepts to practical security measures. Learners will build a strong base for understanding and mitigating adversarial threats in AI systems. This course is part one of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Adversarial AI Attacks, Mitigations, and Defense Strategies, by John Sotiropoulos. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.
