As deep learning models become increasingly powerful, they also become harder to interpret. For high-stakes applications in healthcare, finance, security, and law, it’s not enough to make predictions—you must explain them. This course equips you with the essential skills to build transparent, trustworthy AI systems.

Explore the foundations of explainability, including model introspection, saliency maps, SHAP, LIME, counterfactuals, and surrogate models. Learn to visualize the decision process of CNNs, transformers, and recurrent architectures. Go beyond accuracy to evaluate model fairness, detect bias, and communicate results to both technical and non-technical stakeholders.

Hands-on labs will guide you in debugging deep learning systems, conducting failure analyses, and implementing post-hoc explanation pipelines. Whether you’re a researcher, developer, or data scientist working in sensitive domains, this course will make you fluent in the language of responsible AI.

You’ll leave the course with not just models that work—but models that inspire confidence.

Delivery
Courses are delivered 100% online. Learn on your schedule — videos, case studies, and templates are available instantly upon enrollment. All content is optimized for mobile and desktop.

Refunds
We offer a full refund within 30 days if you’re not satisfied with your ability to interpret and debug model predictions.

Language
English

Curriculum
Module 1: Why Explainability Matters – Ethics, trust, and the need for interpretability.
Module 2: Visualizing Deep Models – Saliency maps, attention visualization, class activation mapping.
Module 3: Post-Hoc Methods & Surrogate Models – LIME, SHAP, counterfactuals, interpretable proxies.
Module 4: Auditing, Bias & Regulatory Use – Fairness metrics, debugging black boxes, communicating results.
Capstone Audit Project: Audit a deep learning model using a suite of interpretability tools, and compile a formal trust report with explanations, visualizations, and recommendations.