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
Overview of Explainable AI
- Defining Explainable AI (XAI)
- The role of transparency in AI modeling
- Primary obstacles in AI interpretability
Core XAI Methodologies
- Agnostic approaches: LIME, SHAP
- Model-specific interpretability strategies
- Deciphering decisions from black-box systems
Practical Application of XAI Tools
- Introduction to open-source XAI libraries
- Integrating XAI into basic machine learning models
- Visualizing model explanations and behavior
Challenges in Explainability
- Balancing accuracy against interpretability
- Constraints of existing XAI methods
- Addressing bias and fairness in transparent models
Ethical Dimensions of XAI
- Understanding the ethical impact of AI transparency
- Harmonizing explainability with performance
- Privacy and data security considerations in XAI
Practical XAI Scenarios
- XAI in medical, financial, and legal sectors
- Compliance and regulatory standards for explainability
- Enhancing trust in AI via transparency
Advanced XAI Concepts
- Investigating counterfactual explanations
- Interpreting neural and deep learning networks
- Analyzing complex AI systems
Emerging Trends in Explainable AI
- New developments in XAI research
- Future prospects and hurdles for AI transparency
- The role of XAI in responsible AI evolution
Requirements
- Foundational knowledge of machine learning principles
- Proficiency in Python programming
Target Audience
- Novices in AI
- Enthusiasts of data science
