1. Course Description
This training course provides core and systematic knowledge of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs), covering fundamental principles, Transformer architecture, as well as practical techniques for model training, fine-tuning, and real-world deployment.
Participants will gain hands-on experience in using and integrating LLMs for practical use cases such as data analytics, financial analysis, and document processing. The course also enables learners to develop a clear understanding of potential risks, ethical considerations, and emerging trends of Generative AI in key application domains.
2. Learning Outcomes
Upon completion of the course, participants are expected to acquire the following knowledge and competencies:
• Gain a comprehensive understanding of the fundamentals of Generative AI and Large Language Models (LLMs), from core operating principles to modern Transformer-based architectures
• Develop the ability to apply and evaluate LLMs in practical scenarios such as document analysis, financial reporting, and professional content drafting
• Understand the end-to-end workflow of LLM training, fine-tuning, and deployment, including prompt engineering techniques and Retrieval-Augmented Generation (RAG)
• Identify and assess risks, ethical issues, security concerns, and common attack vectors associated with the use of LLMs
• Establish clear orientation for the effective application of GenAI and LLMs in professional domains such as finance, taxation, and customs operations
3. Course Structure and Key Modules
• Introduction to Generative Artificial Intelligence
• Introduction to Large Language Models (LLMs)
• Principles of LLM Operation
• Transformer Architecture
• Modern LLM Architectures
• Optimization Techniques in LLMs
• Scaling Laws
• LLM Training
• LLM Fine-tuning
• LLM Deployment and Applications
• Ethical Issues in LLMs
• Risks Associated with the Use of LLMs
• Attacks on LLMs
• The Future of Generative AI
• Applications of Generative AI and LLMs in Professional Operations
4. Duration: 4 days per class
5. Certification Organization: The International Society of Data Scientists (ISODS)
