Generative AI with Large Language Models

• Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment
• Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases
• Use empirical scaling laws to optimize the model’s objective function across dataset size, compute budget, and inference requirements
• Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project
• Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners
• Receive a Coursera certificate demonstrating your skills upon completion of the course

• For data scientists: Gain deeper knowledge into the underlying structure and mechanisms of generative AI and explore avenues for further innovations in this field.
• For machine learning engineers: Learn how to better train, optimize and fine tune generative models while learning about different use cases and applications.
• For prompt engineers: Explore advanced prompting techniques and learn how to control your output using generative configuration parameters.
• For research engineers: Explore the state of art generative models and architectures in depth to build on top of with your own advanced techniques in generative AI.
• For anyone interested in generative AI: Get an extensive introduction to developing with generative AI and its fundamentals.

This is an intermediate course, so you should have some experience coding in Python to get the most out of it. You should also be familiar with the basics of machine learning, such as supervised and unsupervised learning, loss functions, and splitting data into training, validation, and test sets. If you have taken the Machine Learning Specialization or Deep Learning Specialization, you’ll be ready to take this course and dive deeper into the fundamentals of generative AI.