Explainable AI Courses and Certifications

How Can You Trust Machine Learning?

Explore a framework for trustworthy AI based on Clarity, Competence, and Alignment. Learn about explainable ML, rigorous model evaluation, and aligning AI behaviors with human values.

• YouTube
• 52 minutes
• On-Demand
• Free Video

Microcredential

Explainable AI (XAI)

Develop transparent and trustworthy AI systems for high-risk domains. Master XAI concepts, interpretable machine learning, and advanced explainability techniques through hands-on programming and case studies.

• Coursera
• On-Demand
• Paid Course
• 3 courses

Developing Explainable AI (XAI)

Gain skills to develop transparent, trustworthy AI systems using Explainable AI techniques. Learn key concepts, evaluate approaches, and apply XAI to emerging trends like Generative AI.

• Coursera
• 8 hours 26 minutes
• On-Demand
• Free Online Course (Audit)

Explainable Machine Learning (XAI)

Develop transparent and trustworthy AI solutions using explainable machine learning techniques. Implement local and global explainability methods, visualize neural networks, and explore emerging approaches for large language models.

• Coursera
• 14 hours 33 minutes
• On-Demand
• Free Online Course (Audit)

Explainable Artificial Intelligence (XAI) Concepts

Understand the role and real-world realities of Explainable Artificial Intelligence (XAI) with this beginner friendly course.

• DataCamp
• 1 hour
• On-Demand
• Free Trial Available

Explainable AI in Python

Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

How Can I Explain This to You? An Empirical Study of Deep Neural Net Explanation Methods - Spring 2021

Empirical study comparing deep neural network explanation methods, examining usability, stability, and privacy risks across various visual explanation frameworks.

• YouTube
• 37 minutes
• On-Demand
• Free Video

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Explore techniques for interpreting and explaining deep learning models, focusing on LRP rules, Taylor decomposition, and properties of effective explanations in AI systems.

• YouTube
• 32 minutes
• On-Demand
• Free Video

Explore techniques to interpret deep neural networks, understand AI decision-making, and learn about legal rights to explanation in responsible AI development.

• Codecademy
• 1 hour
• On-Demand
• Free Trial Available

Open Data Science

Explainable AI - Methods, Applications & Recent Developments

Explore Explainable AI methods, applications, and developments with Dr. Samek. Learn about Layer-wise Relevance Propagation for various data types and neural architectures, and discuss challenges in the field.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Explainable Structured Machine Learning in Similarity, Graph and Transformer Models

Explores techniques for explaining complex machine learning models, focusing on similarity, graph, and transformer structures. Discusses applications in knowledge evolution, gender bias, and task-solving.

• YouTube
• 53 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Towards Higher-Order and Disentangled Explainable AI

Explore advanced techniques in Explainable AI, including higher-order explanations and disentangled components, to enhance the interpretability of complex machine learning models.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

AI for the Sciences - Recent Developments for XAI - IPAM at UCLA

Explore recent advancements in Explainable AI for scientific applications, focusing on novel insights and developments presented by Klaus-Robert Müller at IPAM's workshop.

• YouTube
• 47 minutes
• On-Demand
• Free Video

1littlecoder

Interpretable Machine Learning Use-Cases - Kaggle 30 Days of ML Day 15

Explore machine learning explainability, interpretability, and use cases for model insights. Learn about feature importance, debugging, and building trust in AI systems.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Alan Turing Institute

Turing Lecture - Algorithmic Accountability - Professor Ben Shneiderman, University of Maryland

Exploring algorithmic accountability and human-centered oversight for safer AI systems. Emphasizes comprehensible, predictable, and controllable designs to prevent failures and improve safety in critical services.

• YouTube
• 1 hour 22 minutes
• On-Demand
• Free Video