Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI)
- The role of transparency in AI models
- Primary challenges in AI interpretability
Foundational XAI Techniques
- Model-agnostic approaches: LIME, SHAP
- Model-specific explainability methods
- Interpreting decisions from black-box models
Practical XAI Tools
- Overview of open-source XAI libraries
- Integrating XAI into simple machine learning models
- Visualizing explanations and model behavior
Navigating Explainability Challenges
- Trade-offs between accuracy and interpretability
- Constraints of current XAI methodologies
- Addressing bias and fairness in explainable models
Ethical Dimensions of XAI
- Evaluating the ethical implications of AI transparency
- Striking a balance between explainability and model performance
- Privacy and data protection considerations in XAI
Practical XAI Applications
- XAI in healthcare, finance, and law enforcement
- Regulatory mandates for explainability
- Fostering trust in AI systems through transparency
Advanced XAI Principles
- Exploring counterfactual explanations
- Interpreting neural networks and deep learning models
- Understanding complex AI systems
Future Directions in Explainable AI
- Novel techniques in XAI research
- Prospective challenges and opportunities for AI transparency
- The influence of XAI on responsible AI development
Requirements
- Foundational knowledge of machine learning concepts
- Working proficiency with Python programming
Target Audience
- Individuals new to AI
- Data science professionals
