Explainable AI Courses and Certifications

Open and Understandable - Designing Explainable AI for Everyone

Discover how to build transparent AI systems using modular models and knowledge graphs that provide clear, step-by-step explanations for every decision made.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Explainable AI - Technical Explanation and Transparency in AI Systems

Discover the fundamentals of Explainable AI, exploring transparency techniques to demystify black-box algorithms and understand AI decision-making processes.

• YouTube
• 54 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Explainable AI in Law and Justice

Discover how explainable AI transforms legal systems, exploring guidelines and contextual applications for transparent decision-making in law and justice.

• YouTube
• 37 minutes
• Self-Paced
• Free Video

Explainable Machine Learning (XAI)

29 ratings at Coursera

Implement LIME, SHAP, ICE, PDP, and ALE explanations in Python, visualize neural network activations, and critique attention and saliency methods for transformers and LLMs.

• Coursera
• 3 weeks, 5 hours a week
• Self-Paced
• Paid Course

Explainable Deep Learning Models for Healthcare

15 ratings at Coursera

Explain deep learning decisions in healthcare: apply permutation feature importance, LIME, SHAP, CAM, Grad-CAM, integrated gradients, and visualised attention weights to time-series models.

• Coursera
• 4 weeks, 8 hours a week
• Self-Paced
• Paid Course

Explainable Deep Learning Models for Healthcare

15 ratings at Coursera

Explain deep learning decisions in healthcare: apply permutation feature importance, LIME, SHAP, CAM and Grad-CAM, integrated gradients, and visualise attention weights in recurrent models.

• Coursera
• 4 weeks, 8 hours a week
• Self-Paced
• Paid Course

Capstone Assignment

Build and explain mortality prediction models on MIMIC-III critical care data, applying permutation feature importance, LIME or Grad-CAM to logistic regression and LSTM models.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course

Capstone Assignment

This course is a capstone assignment requiring you to apply the knowledge and skill you have learnt throughout the specialization. In this course you will choose one of the areas and complete the assignment to pass.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Engineer & Explain AI Model Decisions

Turn messy chat logs into model-ready tensors with TF-IDF and embeddings, then use SHAP to diagnose misclassifications, spurious correlations, and bias in a stakeholder report.

• Coursera
• 2 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Explain Black-Box Models

Explain black-box models: apply SHAP, LIME, counterfactual and surrogate explanations, compare them on fidelity and stability, and build executive-ready feature-importance visuals.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course

Edureka

Explainability Methods & Evaluation

Apply SHAP, surrogate and rule-based methods, and counterfactual explanations to black-box models, then measure explanation fidelity, stability, and robustness under perturbations and adversarial manipulation.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

AI Ethics

26626 ratings at DataCamp

Get an Overview of AI Ethics This introductory course on AI ethics provides a comprehensive overview of ethical considerations in the fast-paced domain of artificial intelligence.

• DataCamp
• 1 hour
• Self-Paced
• Free Trial Available

Responsible AI for Developers: Interpretability & Transparency

Explore practical methods and tools to achieve interpretability and transparency in both data and AI models.

• edX
• 3 weeks, 3-4 hours a week
• Self-Paced
• Free Online Course (Audit)

Responsible AI for Developers: Interpretability & Transparency

Learn practical approaches and tools for interpreting AI models and improving transparency in data and machine-learning systems.

• Google Skills
• 3 hours
• Self-Paced
• Paid Course

UofU Data Science

Local Explanations for Deep Learning Models - Fall 2023

Explore advanced techniques for interpreting deep learning models through local explanations, covering gradient-based attribution, uncertainty estimation, and human-AI trust.

• YouTube
• 17 hours 58 minutes
• Self-Paced
• Free Video