15 Topics
10 Days
74 Sessions
Programme Overview
Training Description
Who Should Attend
- Business Intelligence and Analytics Professionals
- Data Architects and AI Solution Architects
- Risk Management and Model Risk Professionals
- AI Governance and Responsible AI Professionals
- Compliance and Regulatory Professionals
- Cybersecurity and AI Security Specialists
- Technology and Digital Transformation Managers
- Business Leaders using AI-driven decision systems
- Product Managers responsible for AI/ML products
- Researchers and Academics in AI and Machine Learning
- Professionals involved in model validation and model assurance
- Anyone seeking practical expertise in AI transparency and interpretability
Session Objectives
- Understand the fundamentals of Explainable AI (XAI) and model interpretability.
- Master feature importance techniques for model explanation.
- Utilize model visualization for understanding complex models.
- Implement local explanation methods (LIME, SHAP).
- Design and build global explanation models.
- Optimize model explanations for clarity and accuracy.
- Troubleshoot and address interpretability challenges.
- Implement model validation using interpretability metrics.
- Integrate XAI into real-world AI applications.
- Understand how to communicate model explanations effectively.
- Explore advanced XAI techniques (e.g., counterfactual explanations).
- Apply real world use cases for XAI in various domains.
- Leverage XAI libraries for efficient model explanation.
About the Course
This advanced training course provides participants with a practical understanding of Explainable Artificial Intelligence (XAI), model interpretability, and AI transparency. As organizations increasingly rely on machine learning models for decision-making, the ability to understand, explain, validate, and communicate how these models produce their predictions has become essential.
The course explores key concepts and techniques for interpreting both traditional machine learning and complex black-box models. Participants will gain hands-on experience with leading explainability approaches, including SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), while learning how to distinguish between global and local model explanations.Participants will also examine feature importance, partial dependence, individual prediction explanations, model behavior, bias detection, fairness, transparency, and explainability challenges associated with complex AI systems. Practical case studies will demonstrate how XAI can support responsible AI, regulatory compliance, risk management, stakeholder confidence, and better business decision-making.
Curriculum & Topics
15 Topics | 74 Sessions
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Workshop 1.1:
Fundamentals of Explainable AI (XAI) and model interpretability.
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Workshop 1.2:
Overview of feature importance, visualization, and explanation methods.
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Workshop 1.3:
Setting up an XAI development environment.
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Workshop 1.4:
Introduction to XAI libraries and tools.
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Workshop 1.5:
Best practices for model interpretability.
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Workshop 2.1:
Implementing feature importance using permutation importance.
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Workshop 2.2:
Utilizing SHAP values for feature attribution.
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Workshop 2.3:
Designing and building feature importance analysis pipelines.
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Workshop 2.4:
Optimizing feature importance for model understanding.
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Workshop 2.5:
Best practices for feature importance.
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Workshop 3.1:
Implementing model visualization techniques.
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Workshop 3.2:
Utilizing partial dependence plots (PDPs) and ICE plots.
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Workshop 3.3:
Designing and building model visualization dashboards.
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Workshop 3.4:
Optimizing visualizations for model transparency.
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Workshop 3.5:
Best practices for model visualization.
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Workshop 4.1:
Implementing LIME for local model explanations.
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Workshop 4.2:
Utilizing SHAP for local feature attribution.
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Workshop 4.3:
Designing and building local explanation pipelines.
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Workshop 4.4:
Optimizing local explanations for individual predictions.
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Workshop 4.5:
Best practices for local explanations.
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Workshop 5.1:
Designing and building global explanation models.
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Workshop 5.2:
Utilizing surrogate models for global interpretation.
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Workshop 5.3:
Implementing rule-based explanations.
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Workshop 5.4:
Optimizing global explanations for model understanding.
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Workshop 5.5:
Best practices for global explanations.
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Workshop 6.1:
Optimizing model explanations for clarity and accuracy.
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Workshop 6.2:
Utilizing evaluation metrics for explanation quality.
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Workshop 6.3:
Designing and building explanation pipelines.
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Workshop 6.4:
Optimizing explanations for specific audiences.
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Workshop 6.5:
Best practices for explanation optimization.
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Workshop 7.1:
Debugging issues in model explanations.
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Workshop 7.2:
Analyzing inconsistencies and biases in explanations.
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Workshop 7.3:
Utilizing troubleshooting techniques for explanation improvement.
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Workshop 7.4:
Resolving common interpretability challenges.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing model validation using interpretability metrics.
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Workshop 8.2:
Utilizing explanation-based model evaluation.
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Workshop 8.3:
Designing and building validation pipelines.
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Workshop 8.4:
Optimizing model validation for explanation quality.
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Workshop 8.5:
Best practices for model validation.
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Workshop 9.1:
Integrating XAI into real-world AI applications.
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Workshop 9.2:
Utilizing APIs and deployment tools for XAI.
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Workshop 9.3:
Implementing real-time model explanation systems.
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Workshop 9.4:
Optimizing XAI for deployment environments.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Communicating model explanations effectively.
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Workshop 10.2:
Utilizing visualizations and narratives for explanation.
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Workshop 10.3:
Designing and building explanation reports and presentations.
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Workshop 10.4:
Optimizing communication for stakeholder understanding.
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Workshop 10.5:
Best practices for communication.
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Workshop 11.1:
Implementing counterfactual explanations.
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Workshop 11.2:
Utilizing causal explanations for model behavior.
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Workshop 11.3:
•esigning and building advanced XAI pipelines.
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Workshop 11.4:
Optimizing advanced techniques for specific applications.
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Workshop 11.5:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing XAI in financial risk assessment.
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Workshop 12.2:
Utilizing XAI in medical diagnosis.
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Workshop 12.3:
Implementing XAI in legal decision-making.
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Workshop 12.4:
Utilizing XAI in customer service chatbots.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing SHAP and LIME libraries for model explanations.
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Workshop 13.2:
Implementing XAI tools with TensorFlow and PyTorch.
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Workshop 13.3:
Designing and building explanation pipelines with libraries.
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Workshop 13.4:
Optimizing library usage for efficient explanation.
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Workshop 13.5:
Best practices for library implementation.
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Workshop 14.1:
Implementing ethical considerations in model explanations.
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Workshop 14.2:
Utilizing fairness and bias detection techniques.
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Workshop 14.3:
Designing and building ethical XAI frameworks.
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Workshop 14.4:
Optimizing explanations for ethical compliance.
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Workshop 14.5:
Best practices for ethical considerations.
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Workshop 15.1:
Emerging trends in explainable AI.
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Workshop 15.2:
Utilizing automated XAI tools.
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Workshop 15.3:
Implementing interactive and dynamic model explanations.
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Workshop 15.4:
Best practices for future XAI.
