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

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

Developing Explainable AI (XAI)

24 ratings at Coursera

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 31 minutes
• On-Demand
• Free Online Course (Audit)

Explainable Machine Learning (XAI)

17 ratings at Coursera

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 38 minutes
• On-Demand
• Free Online Course (Audit)

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

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

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

Towards Explainable and Reliable AI Models for Optimization

Explore cutting-edge approaches to developing transparent and dependable AI optimization models, focusing on explainability and reliability in machine learning applications.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

XAI: Explainable AI

222 ratings at Udemy

How to explain your machine learning models in Python

• Udemy
• 2 hours 2 minutes
• On-Demand
• Paid Course

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

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

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

Alan Turing Institute

AIUK 2022 Workshop - ExplAIN: AI Explainability in Practice

Explore AI explainability principles, biases, and ethical considerations in recruitment through an interactive workshop on practical implementation and fairness.

• YouTube
• 57 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