Interpreting Image Classifiers with LIME

As machine learning models grow more complex, understanding why an image classifier made a specific decision becomes critical for building trust and ensuring fairness. This text-based course guides you through the foundational concepts of Explainable AI (XAI) using LIME to demystify black-box neural networks. You will transition from treating computer vision models as mysterious black boxes to actively interpreting, validating, and explaining their predictions using local surrogate models. What you'll learn: * Understand the core principles of Explainable AI and why model interpretability is essential. * Explain how LIME perturbs image data to analyze local decision boundaries. * Generate and interpret explanations to identify which image features drive classification decisions. * Identify bias and debugging opportunities in image classifiers by evaluating their explanations. * Apply interpretability workflows to modern deep learning architectures using clear, step-by-step code walkthroughs. You will start with the fundamental terminology of explainability before moving on to the mechanics of local surrogate models, perturbation techniques, and practical evaluation methods. This course is designed for beginners in machine learning and data science, requiring only basic Python knowledge and no prior experience with explainable AI. Start reading today to make your computer vision models transparent, accountable, and interpretable.