This course delves into the crucial aspect of AI explainability, focusing on techniques and methodologies to make AI systems transparent and interpretable. Participants will explore various modules covering topics such as key concepts in explainable AI (XAI), regulatory landscape, building explainable models, post-hoc interpretability techniques, challenges in deep learning, explainability in natural language processing (NLP) and image recognition, fairness and bias, human-centered design, real-world applications, ethics, legal considerations, AI development lifecycle, emerging trends, and continuous learning strategies.

Who This Course Is For:

This course is designed for data scientists, machine learning engineers, AI researchers, policymakers, and professionals working with AI technologies who are interested in understanding and implementing explainable AI solutions. It is suitable for individuals seeking to enhance their knowledge of AI explainability techniques and ensure transparency, accountability, and trustworthiness in AI systems across various applications and industries.