Length: 2 Days
The Certified Generative AI and Large Language Models Specialist (CGALLMS™) certification is designed to provide in-depth knowledge and practical skills in the cutting-edge fields of Generative AI and Large Language Models. It covers the technical aspects, ethical considerations, and strategic applications of these technologies in various industries.
Objectives:
- To deepen understanding of GenAI and LLM technologies and their operational mechanics.
- To equip professionals with the skills to develop, implement, and manage GenAI and LLM solutions.
- To address the ethical and societal impacts of deploying generative AI and LLMs.
- To foster innovation and strategic thinking in applying GenAI and LLMs across different sectors.
Target Audience:
- AI and machine learning engineers and developers specializing in GenAI and LLMs.
- Data scientists and analysts working with generative models and language processing.
- IT and technology strategists planning to integrate GenAI and LLMs into business operations.
- Ethicists and policy makers focusing on AI ethics and governance.
Course Outlines:
Module 1: Introduction to Generative AI and Large Language Models
- Overview of Generative AI
- Introduction to Large Language Models
- Historical Development and Milestones
- Key Concepts and Terminology
- Applications and Use Cases
- Ethical Considerations and Challenges
Module 2: Fundamentals of Generative AI
- Neural Networks and Deep Learning Basics
- Sequence Modeling Techniques
- Autoencoders and Variational Autoencoders (VAEs)
- Generative Adversarial Networks (GANs)
- Reinforcement Learning in Generative AI
- Evaluation Metrics and Techniques
Module 3: Large Language Models Architecture and Training
- Transformer Architecture Overview
- Attention Mechanisms and Self-Attention
- Pre-training Strategies and Datasets
- Fine-tuning Techniques
- Handling Long Sequences and Context
- Model Compression and Optimization
Module 4: Advanced Techniques in Generative AI
- Conditional Generation
- Style Transfer and Manipulation
- Controllable Generation
- Multi-Modal Generation
- Transfer Learning Across Domains
- Adversarial Robustness and Security
Module 5: Applications and Industry Implementations
- Natural Language Understanding and Generation
- Creative Content Generation
- Chatbots and Conversational Agents
- Recommendation Systems
- Healthcare and Biomedical Applications
- Financial Modeling and Predictions
Module 6: Ethics, Bias, and Responsible AI
- Bias and Fairness in Generative AI
- Privacy Concerns and Data Ethics
- Mitigating Risks of Harmful Content Generation
- Transparency and Explainability
- Regulatory Landscape and Compliance
- Social Implications and Future Directions
Exam Domains:
- Fundamentals of Generative AI
- Large Language Models Architecture
- Training and Fine-Tuning Techniques
- Ethical Considerations and Bias Mitigation
- Applications and Use Cases
Question Types:
- Multiple Choice: Assessing knowledge of key concepts, definitions, and principles.
- True/False: Evaluating understanding of factual statements related to generative AI and large language models.
- Short Answer: Testing comprehension and ability to explain concepts in a concise manner.
- Scenario-Based: Presenting real-world situations to assess problem-solving skills and application of knowledge.
- Essay: Allowing candidates to elaborate on complex topics, provide analysis, and demonstrate critical thinking.
Passing Criteria:
- Overall passing score: Candidates must achieve a minimum passing score across all exam domains.
- Domain-specific passing score: Candidates must also meet a minimum passing score for each individual domain.
- Performance consistency: Candidates should demonstrate a balanced understanding across all domains, with no significantly weak areas.
- Ethical considerations: Candidates must exhibit an understanding of ethical issues surrounding generative AI and large language models, with appropriate responses to scenarios involving ethical dilemmas.