| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 4 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹5499 / $82 |
| Tools | Python TensorFlow PyTorch Keras Scikit-learn Jupyter Notebook Google Colab Hugging Face |
About the AI Ethics and Explainable AI (XAI) in Healthcare Course
This program emphasizes the ethical considerations of using AI in healthcare, such as fairness, bias, accountability, and patient privacy.
Additionally, it covers Explainable AI (XAI) methodologies that ensure transparency, allowing healthcare professionals and patients to understand the decisions made by AI systems. The program integrates hands-on experience with case studies of AI implementations in healthcare, addressing ethical dilemmas and regulatory frameworks.
Program Highlights
• Comprehensive coverage of AI Ethics and Explainable AI (XAI) in Healthcare from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Artificial Intelligence
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Exposure to industry-standard tools and platforms used in Artificial Intelligence
• Career-oriented training for academic and professional growth in Artificial Intelligence
Course Curriculum
Module 1: Introduction to AI Ethics and Explainable AI (XAI) in Healthcare
- Overview and historical evolution of AI Ethics and Explainable AI (XAI) in Healthcare
- Key terminology, definitions, and core concepts in Artificial Intelligence
- Current industry landscape, trends, and career opportunities
- Setting up the learning environment and essential tools
Module 2: Fundamentals and Theoretical Foundations
- Core principles and scientific/theoretical underpinnings of AI Ethics and Explainable AI (XAI) in Healthcare
- Mathematical and analytical frameworks relevant to Artificial Intelligence
- Comparative analysis of major approaches and methodologies
- Understanding key standards, guidelines, and best practices
Module 3: Neural Networks
- Introduction to Neural Networks concepts and methodologies
- Step-by-step practical implementation of Neural Networks techniques
- Tools and platforms commonly used for Neural Networks
- Troubleshooting, optimization, and best practices
Module 4: Deep Learning
- Introduction to Deep Learning concepts and methodologies
- Step-by-step practical implementation of Deep Learning techniques
- Tools and platforms commonly used for Deep Learning
- Troubleshooting, optimization, and best practices
Module 5: NLP
- Introduction to NLP concepts and methodologies
- Step-by-step practical implementation of NLP techniques
- Tools and platforms commonly used for NLP
- Troubleshooting, optimization, and best practices
Module 6: Advanced Topics and Emerging Trends in Artificial Intelligence
- Cutting-edge research and innovations in AI Ethics and Explainable AI (XAI) in Healthcare
- Integration with AI, automation, and modern technologies
- Industry case studies and real-world problem solving
- Future directions and career pathways in Artificial Intelligence
Module 7: Capstone Project and Assessment
- End-to-end project implementation using AI Ethics and Explainable AI (XAI) in Healthcare skills
- Peer review, collaborative exercises, and expert feedback
- Portfolio-ready project documentation and presentation
- Final assessment and course completion evaluation
Tools, Techniques, or Platforms Covered
Python TensorFlow PyTorch Keras Scikit-learn Jupyter Notebook Google Colab Hugging Face
Real-World Applications
- Apply AI Ethics and Explainable AI (XAI) in Healthcare skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Artificial Intelligence competencies
- Solve industry-relevant problems using AI Ethics and Explainable AI (XAI) in Healthcare methodologies and tools
- Contribute to open-source projects and collaborative research in Artificial Intelligence
- Prepare for competitive examinations, interviews, and professional certifications in Artificial Intelligence
Who Should Attend & Prerequisites
- Students pursuing degrees in Artificial Intelligence, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into Artificial Intelligence roles
- Researchers and academicians looking to adopt modern techniques in Artificial Intelligence
- Entrepreneurs, freelancers, and self-learners interested in practical Artificial Intelligence knowledge
Certification
