What you will learn in Generative AI with Large Language Models Course
- Understand the fundamentals of generative AI and the lifecycle of large language models (LLMs), including data gathering, model selection, performance evaluation, and deployment.
- Gain in-depth knowledge of transformer architectures, their training processes, and how fine-tuning enables adaptation to specific use cases.
- Apply empirical scaling laws to optimize model objectives concerning dataset size, computational resources, and inference requirements.
- Implement state-of-the-art training, tuning, inference, and deployment methods to maximize model performance within project constraints.
- Explore real-world applications and challenges of generative AI through insights from industry researchers and practitioners.
Program Overview
Generative AI Use Cases, Project Lifecycle, and Model Pre-training
⏱️ 5 hours
- Introduction to generative AI and LLMs, their use cases, and tasks.
- Understanding the transformer architecture and text generation techniques.
- Exploration of the generative AI project lifecycle and model pre-training processes.
- Hands-on lab: Summarize dialogue using generative AI.
Fine-tuning and Evaluating Large Language Models
⏱️ 4 hours
- Techniques for fine-tuning LLMs with instruction datasets.
- Understanding parameter-efficient fine-tuning (PEFT) and addressing catastrophic forgetting.
- Evaluation methods for LLM performance.
- Hands-on lab: Fine-tune a generative AI model for dialogue summarization.
Reinforcement Learning and LLM-powered Applications
⏱️5 hours
- Introduction to reinforcement learning with human feedback (RLHF) for LLMs.
- Techniques like chain-of-thought prompting to enhance reasoning and planning abilities.
- Addressing challenges such as knowledge cut-offs and implementing information retrieval strategies.
- Hands-on lab: Fine-tune FLAN-T5 with reinforcement learning to generate more positive summaries.
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Job Outlook
- Proficiency in generative AI and LLMs is increasingly sought after in roles such as AI Developer, Machine Learning Engineer, and Data Scientist.
- Understanding transformer architectures and fine-tuning techniques positions learners for opportunities in cutting-edge AI research and application development.
- Skills acquired are applicable across industries leveraging AI for natural language processing, content generation, and automation.
Specification: Generative AI with Large Language Models
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FAQs
- The course consists of 3 modules spread across 3 weeks, with a workload of approximately 5–10 hours per week—totaling around 16 hours.
- Module breakdown:
- Week 1 (≈5 h): Generative AI use cases, project lifecycle, and model pre-training.
- Week 2 (≈8 h): Fine-tuning and evaluating large language models.
- Week 3 (≈10 h): Reinforcement learning and LLM-powered applications.
- It’s self-paced, allowing you to adapt the timeline to your schedule.
- Offers flexibility to complete it faster if you’re able, or spread it out more slowly if needed.
- This is an intermediate course—you should have prior experience in Python and basic machine learning, including supervised learning, loss functions, and data splitting.
- If you’re new to programming or ML, consider starting with a foundational course first, such as the Machine Learning Specialization by DeepLearning.AI.
- Familiarity with Python programming, PyTorch or TensorFlow, and core ML concepts will help you get the most from the content.
- The course includes 3 assignments, one associated with each module, to reinforce learning through application.
- These likely include notebook-based coding labs, often preconfigured, allowing you to focus on concepts without setup headaches.
- Labs are straightforward to use—common feedback is that students simply run existing code cells, typically within a 2-hour time frame.
- You’ll gain a practical understanding of how generative AI works, from lifecycle management to real-world deployment.
- Learn how businesses use LLMs—covering value creation and performance considerations.
- Build skills in prompt engineering, model tuning, reinforcement learning applications, and performance optimization.
- Earn a shareable certificate that can be added to LinkedIn or resumes, enhancing your professional profile.
Strengths:
- Taught by industry experts from DeepLearning.AI and AWS, with practical insight into real-world applications.
- High learner satisfaction—rated 4.8/5, with 95% of users recommending the course.
- Labs are ready-to-use, minimizing setup complexity and broken dependencies.
Limitations:
- The community engagement is modest—forums exist but discussions are not highly active.
- As an introductory-level course, it doesn’t dive deep into advanced deployment pipelines or production-grade LLM infrastructure.
- For developers seeking full production workflows or custom model training from scratch, follow-up courses may be needed.
