Explainable AI: Engineering & Interpreting Model Decisions

As artificial intelligence systems become more prevalent, understanding how they arrive at their decisions is crucial for building trust and ensuring ethical deployment. This course provides a foundational understanding of Explainable AI (XAI) principles and practical methods to interpret and engineer more transparent machine learning models. What you'll learn: * Understand core concepts and terminology of Explainable AI (XAI). * Apply techniques like LIME and SHAP to interpret individual model predictions. * Evaluate feature importance and understand global model behavior. * Identify and mitigate potential biases in AI models through explainability techniques. * Practice interpreting explanations for various machine learning algorithms. * Build more trustworthy and transparent AI systems from the ground up. The course begins with the fundamental concepts of AI interpretability, then progresses to practical methods for explaining local and global model behaviors, and concludes with considerations for building ethical AI systems. This course is designed for absolute beginners with no prior experience in Explainable AI or advanced machine learning concepts. Start your journey to building more transparent and trustworthy AI systems today.