kodestree’s Explainable AI (XAI) Course introduces the principles, methods, and tools used to make machine learning and deep learning models transparent and interpretable. The course explores local and global explanation techniques, feature importance analysis, model-agnostic interpretability, bias detection, fairness evaluation, and responsible AI practices. You will gain practical knowledge of widely adopted XAI frameworks such as SHAP and LIME to interpret model predictions, validate AI systems, improve stakeholder trust, and support regulatory and ethical AI requirements across real-world applications.
Learning Objectives
By the end of this course, you’ll be able to:
- Understand the core concepts, principles, and significance of Explainable AI (XAI).
- Interpret and explain predictions generated by machine learning and deep learning models.
- Apply SHAP and LIME techniques for feature attribution and model explanation.
- Evaluate feature importance using both local and global interpretability methods.
- Analyze model behavior to identify influential factors affecting predictions.
- Detect bias and evaluate fairness in AI and machine learning models.
- Perform AI model auditing, validation, and transparency assessments.
- Implement responsible AI practices to improve trust, accountability, and compliance.
- Communicate AI model decisions effectively to technical teams, business stakeholders, and decision-makers.
Prerequisites
- Python Programming
- NumPy and Scikit-learn
- Machine Learning Fundamentals
- Supervised Learning Models
- Basic Neural Networks
- Linear Algebra
- Probability and Statistics
What Will You Learn
- Explainable AI (XAI) Fundamentals
- Model Interpretability Techniques
- Global and Local Explanations
- SHAP Explanations
- LIME Explanations
- Feature Importance Analysis
- Partial Dependence Plots (PDP)
- Bias Detection and Fairness Evaluation
- Explainability for Machine Learning Models
- Explainability for Deep Learning Models
- Model Auditing and Transparency
- Responsible AI and Governance
Who Should Do This Course
- Data Scientists
- Machine Learning Engineers
- AI Engineers
- Data Analysts
- AI Researchers
- Deep Learning Engineers
- MLOps Engineers
- AI Product Managers
- Risk and Compliance Professionals
- AI Software Engineers
