Explain Black-Box Models

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Explain Black-Box Models

This course is part of AI Techniques, Causal Inference & Business Optimization Specialization

Instructor: Hurix Digital

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Explainability as Communication: XAI is valuable only when it turns complex model behavior into clear, actionable insights stakeholders can trust.
Empirical Method Selection: SHAP, LIME, and counterfactuals should be chosen using fidelity and stability tests, not popularity.
Stakeholder Alignment: The best explanation method depends on stakeholder needs and use cases, not just technical accuracy.
Fidelity for Quality Assurance: Fidelity metrics show how accurately explanations reflect true model behavior in production.

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Build your subject-matter expertise

This course is part of the AI Techniques, Causal Inference & Business Optimization Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
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There are 3 modules in this course

Ready to unlock the mystery behind your most powerful models? This Short Course was created to help data analysis professionals accomplish transparent and trustworthy AI implementation. By completing this course, you'll master SHAP values for executive communication, systematically compare explainability methods, and align explanation strategies with stakeholder needs.

By the end of this course, you will be able to: Apply SHAP values to a black-box model and produce feature-importance visuals interpretable by non-technical executives Evaluate two XAI methods (LIME vs. SHAP) for fidelity and stability on the same model and dataset Apply counterfactual and surrogate-model explanations to the same black-box model and compare stakeholder preference scores Evaluate explanation completeness using fidelity metrics and recommend the superior approach This course is unique because it bridges advanced explainability techniques with business communication, ensuring complex model insights drive informed decision-making. To be successful in this project, you should have a background in Python programming and machine learning fundamentals.

Apply SHAP values to black-box models and create executive-ready feature importance visualizations.

What's included

3 videos1 reading1 assignment1 ungraded lab

3 videos Total 9 minutes

Why SHAP Values Transform Executive Decision-Making 2 minutes
SHAP Fundamentals: From Theory to Executive Communication 5 minutes
Building SHAP Visualizations for Executive Presentations 3 minutes

1 reading Total 10 minutes

SHAP Implementation Strategy for Business Applications 10 minutes

1 assignment Total 6 minutes

SHAP Implementation Assessment 6 minutes

1 ungraded lab Total 18 minutes

Executive SHAP Dashboard Creation 18 minutes

Evaluate and compare LIME vs SHAP methods using fidelity and stability metrics for systematic explainability assessment.

What's included

2 videos2 readings2 assignments

2 videos Total 11 minutes

Fidelity and Stability Metrics for XAI Evaluation 6 minutes
Implementing LIME vs SHAP Comparison Analysis 5 minutes

2 readings Total 22 minutes

LIME vs SHAP: Comprehensive Method Analysis 12 minutes
Podcast: Real-World XAI Method Selection: Industry Case Studies and Lessons Learned 10 minutes

2 assignments Total 24 minutes

LIME vs SHAP Business Evaluation Framework 17 minutes
LIME vs SHAP Evaluation Assessment 7 minutes

Apply counterfactual and surrogate-model explanations while evaluating explanation completeness using fidelity metrics for optimal stakeholder-centered approaches.

What's included

3 videos1 reading2 assignments1 ungraded lab

3 videos Total 12 minutes

When Standard Explanations Fail: The Case for Advanced XAI Methods 2 minutes
Counterfactual and Surrogate Model Explanations: Theory and Implementation 6 minutes
Implementing Counterfactual Explanations for Business Applications 3 minutes

1 reading Total 11 minutes

Advanced XAI Methods: Counterfactuals and Surrogate Models in Business Context 11 minutes

2 assignments Total 52 minutes

Advanced XAI Methods and Completeness Assessment 7 minutes
Explainable AI Mastery Evaluation 45 minutes

1 ungraded lab Total 20 minutes

Comprehensive XAI Method Evaluation and Stakeholder Assessment 20 minutes

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Instructor

Hurix Digital

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