AWS Generative AI Course

The "Generative AI with Large Language Models (LLMs)" course, in collaboration with AWS, offers a comprehensive understanding of generative artificial intelligence and its practical applications. 

The course is designed to equip participants with foundational knowledge, practical skills, and a functional understanding of generative AI. Here are the key aspects covered in the course:

1. Course Overview: The course focuses on teaching participants the fundamentals of generative AI and how to deploy it in real-world scenarios. It aims to bridge the gap between theoretical knowledge and practical implementation.

2. Content: Participants will dive into the latest research on Generative AI, exploring how companies are leveraging cutting-edge technology to create value. The course curriculum is designed to provide a strong grasp of how to develop with Large Language Models (LLMs).

3. Instructor Expertise: The course is led by expert instructors who are AWS AI practitioners actively involved in building and deploying AI solutions in business contexts. Their real-world experience brings practical insights to the course content.

4. Course Objectives:
   - Gain a foundational understanding of generative AI principles and concepts.
   - Explore the transformer architecture that powers Large Language Models (LLMs), including training and fine-tuning.
   - Learn empirical scaling laws to optimize model objectives across different parameters.
   - Apply state-of-the-art training, tuning, inference, and deployment methods to maximize model performance within project constraints.
   - Understand the challenges and opportunities that generative AI presents for businesses through industry stories.

5. Target Audience:
   - Data Scientists: Deepen knowledge of generative AI mechanisms and explore avenues for innovation.
   - Machine Learning Engineers: Enhance skills in training, optimization, and fine-tuning of generative models across various use cases.
   - Prompt Engineers: Learn advanced prompting techniques and output control using generative configuration.
   - Research Engineers: Dive into advanced generative model architectures to develop advanced techniques in generative AI.
   - Anyone Interested in Generative AI: Get an extensive introduction to developing with generative AI and its fundamentals.

6. Prerequisites:
   - Intermediate coding skills in Python.
   - Familiarity with machine learning basics: supervised and unsupervised learning, loss functions, and data splitting.
   - Those who have completed the Machine Learning Specialization or Deep Learning Specialization will have a solid foundation.

7. Certificate: Upon completion of the course, participants will receive a Coursera certificate showcasing their newly acquired skills in generative AI.

The course is tailored to offer both theoretical understanding and hands-on skills, making it suitable for a wide range of learners, from data scientists and machine learning engineers to those interested in exploring the field of generative AI. By enrolling in this course, participants can gain the expertise needed to thrive in the rapidly evolving landscape of AI and machine learning.