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As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy. This course provides a comprehensive introduction to Explainable AI (XAI), empowering you to develop AI solutions that are aligned with responsible AI principles. Through discussions, case studies, and real-world examples, you will gain the following skills: 1. Define key XAI terminology and concepts, including interpretability, explainability, and transparency. 2. Evaluate different interpretable and explainable approaches, understanding their trade-offs and applications. 3. Integrate XAI explanations into decision-making processes for enhanced transparency and trust. 4. Assess XAI systems for robustness, privacy, and ethical considerations, ensuring responsible AI development. 5. Apply XAI techniques to cutting-edge areas like Generative AI, staying ahead of emerging trends. This course is ideal for AI professionals, data scientists, machine learning engineers, product managers, and anyone involved in developing or deploying AI systems. By mastering XAI, you'll be equipped to create AI solutions that are not only powerful but also interpretable, ethical, and trustworthy, solving critical challenges in domains like healthcare, finance, and criminal justice. To succeed in this course, you should have experience building AI products and a basic understanding of machine learning concepts like supervised learning and neural networks. The course will cover explainable AI techniques and applications without deep technical details.