Explainable AI for Industry
Build Trustworthy AI—Make Your Models Explainable, Accountable, and Industry-Ready
Early access to e-LMS included
About This Course
Explainable AI for Industry is a specialized training program focused on interpreting and understanding the decision-making processes of AI/ML models. As AI adoption accelerates across sectors, industries demand more interpretable, fair, and auditable systems. This course addresses the “black box” problem by teaching frameworks, algorithms, and best practices for integrating explainability into AI workflows—helping professionals enhance trust, meet compliance standards, and make responsible AI-driven decisions.
Aim
To equip professionals with the knowledge and tools to implement Explainable AI (XAI) techniques, enabling transparency, trust, and regulatory compliance in real-world industrial applications.
Program Objectives
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To promote transparent and responsible AI in critical sectors
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To bridge the gap between model performance and stakeholder trust
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To enable organizations to meet legal and ethical standards
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To integrate explainability as a core part of AI system development
Program Structure
Week 1: Foundations of Explainable AI
Module 1: Understanding Explainability in AI
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Chapter 1.1: What is Explainable AI?
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Chapter 1.2: Why Explainability Matters in Industry
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Chapter 1.3: Regulatory and Ethical Drivers (GDPR, AI Act)
Module 2: XAI Methods and Taxonomy
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Chapter 2.1: Model-Specific vs. Model-Agnostic Techniques
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Chapter 2.2: Global vs. Local Explanations
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Chapter 2.3: Common Techniques: SHAP, LIME, Anchors, Partial Dependence
Week 2: Tools and Techniques in Practice
Module 3: XAI in Machine Learning Pipelines
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Chapter 3.1: Integrating SHAP in Tree-based Models
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Chapter 3.2: Using LIME for Text and Image Classification
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Chapter 3.3: Visualizing Feature Importance and Decision Boundaries
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Chapter 3.4: Performance vs. Interpretability Trade-Offs
Module 4: Toolkits for Explainability
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Chapter 4.1: Overview of XAI Libraries (SHAP, LIME, ELI5, Captum)
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Chapter 4.2: Explainability in Deep Learning with Captum
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Chapter 4.3: Explainability in Production with MLflow and dashboards
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Chapter 4.4: Case: Interpreting Drift and Model Updates in Production
Week 3: Real-World Deployment and Use Cases
Module 5: Industry Applications of XAI
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Chapter 5.1: Financial Services – Credit Risk, Fraud Detection
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Chapter 5.2: Healthcare – Diagnostics, Clinical Decision Support
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Chapter 5.3: Manufacturing – Predictive Maintenance, QA Automation
Module 6: Auditing, Compliance, and Communication
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Chapter 6.1: Model Auditing and Documentation Practices
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Chapter 6.2: Explaining AI to Non-Technical Stakeholders
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Chapter 6.3: Building Trustworthy AI Pipelines
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Chapter 6.4: Capstone – Develop an XAI Dashboard for Business Decision Makers
Who Should Enrol?
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Data scientists, ML/AI engineers, domain experts, and analytics professionals
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Industry leaders and managers responsible for AI projects
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Compliance, ethics, or governance officers in tech-driven organizations
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Prior understanding of machine learning concepts is recommended
Program Outcomes
By the end of this program, learners will:
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Understand and apply core XAI algorithms and visualization tools
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Evaluate and improve the interpretability of ML models
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Address ethical, regulatory, and trust challenges in AI deployments
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Communicate AI decisions effectively across technical and business teams
Fee Structure
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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