Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Explainable AI (XAI) addresses the question every deployed model eventually faces: why did it decide that? You will work through the leading interpretability techniques — feature importance, SHAP values, LIME, partial-dependence and counterfactual explanations — and learn when each is appropriate for tabular, image and text models. Beyond the tools, the course covers the human and regulatory side: communicating explanations to stakeholders, using them to debug and de-bias models, and meeting transparency expectations under emerging AI regulation. You leave able to make a model’s behaviour legible and defensible. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Explainable AI teaches the methods that make model predictions interpretable — SHAP, LIME, feature attribution and counterfactuals — and how to use them for trust, debugging and compliance.
📋 Course Objectives
1. Apply SHAP, LIME and feature-attribution methods to real models.
2. Interpret tabular, image and text model predictions.
3. Use counterfactual and partial-dependence analysis to explain decisions.
4. Detect and communicate bias and failure modes.
5. Meet transparency expectations for regulated and high-stakes use.
👥 Who Should Enroll?
• Data scientists deploying models in high-stakes settings
• ML engineers debugging and auditing model behaviour
• Risk, compliance and governance professionals
• Researchers studying interpretable machine learning
🚀 Key Learning Outcomes
• The ability to explain any model’s predictions to technical and lay audiences.
• A model-interpretability report you can reuse at work.
• Skills that support AI governance and compliance.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Explainable Ai (Xai) Foundations
Develop a comprehensive understanding of the mathematical foundations of artificial intelligence, including linear algebra, calculus, and probability theory • Analyze the fundamental concepts of machine learning, including supervised, unsupervised, and reinforcement learning, and their applications in XAI • Design simple neural networks using popular deep learning frameworks, such as TensorFlow or PyTorch, to illustrate the basics of AI model development
Data Engineering, Preprocessing, and Feature Pipelines
Configure data pipelines using Apache Beam or Apache Spark to handle large-scale datasets and perform data preprocessing tasks, such as data cleaning and feature scaling • Implement data quality control measures, including data validation, data normalization, and data transformation, to ensure high-quality data for XAI model training • Evaluate the effectiveness of different feature engineering techniques, including feature selection, feature extraction, and feature construction, to improve XAI model performance
Model Architecture, Algorithm Design, and Explainable Ai (Xai) Methods
Design and implement interpretable machine learning models, including decision trees, random forests, and gradient boosting machines, to provide insights into XAI model decisions • Develop and evaluate model-agnostic explanation methods, including saliency maps, feature importance, and partial dependence plots, to provide explanations for complex XAI models • Analyze the trade-offs between model accuracy and model interpretability, and develop strategies to balance these competing objectives in XAI model development
Training, Hyperparameter Optimization, and Evaluation
Implement hyperparameter tuning techniques, including grid search, random search, and Bayesian optimization, to optimize XAI model performance • Evaluate the performance of XAI models using metrics, including accuracy, precision, recall, F1-score, and mean squared error, and develop strategies to improve model performance • Develop and implement model evaluation protocols, including cross-validation, bootstrapping, and walk-forward optimization, to ensure reliable XAI model evaluation
Deployment, MLOps, and Production Workflows
Configure and deploy XAI models using cloud-based platforms, including AWS SageMaker, Google Cloud AI Platform, and Azure Machine Learning • Implement model serving and monitoring pipelines using tools, including TensorFlow Serving, AWS SageMaker Hosting, and Azure Machine Learning Model Management • Develop and implement continuous integration and continuous deployment (CI/CD) pipelines for XAI model development, testing, and deployment
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze the ethical implications of XAI model development and deployment, including fairness, transparency, and accountability • Develop and implement strategies to mitigate bias in XAI models, including data preprocessing, feature engineering, and model regularization techniques • Evaluate the effectiveness of different explainability methods in providing insights into XAI model decisions and develop strategies to improve model transparency
Industry Integration, Business Applications, and Case Studies
Develop and implement XAI solutions for real-world business problems, including customer segmentation, credit risk assessment, and medical diagnosis • Analyze the business value of XAI solutions, including return on investment (ROI) analysis, cost-benefit analysis, and customer satisfaction metrics • Evaluate the effectiveness of different XAI solutions in providing insights into complex business problems and develop strategies to improve XAI model adoption
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Scikit-learn |
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