Introduction to Generative AI by Coursera
Course Highlights
- Learn to utilize Generative AI for automation.
- Develop Generative AI software solutions.
- Build solutions with Prompt Engineering to enhance Generative AI output.
Skills you will learn!
Curriculum
36 Topics
Meet your course instructor: Alfredo Deza
Meet your course instructor: Derek Wales
About this Course
Introduction
What is Generative AI?
Brief history and Evolution of AI
How do Large Language Models Work in applications?
How are Large Language Models created?
Summary
Introduction
What are LLMs and how do they work?
Benefits and risks of using LLMs
Mitigating risks of LLMs
What are foundation models?
Summary
Introduction
OpenAI and ChatGPT
Hugging Face and Open Source models
Using local models
Cloud-based solutions
Summary
Connect with your instructors
Course structure and Discussion Etiquette
Key Terms
History of artificial Intelligence
Understanding Large Language Models
Key Terms
External lab: trigger inaccuracy in a model
Foundation models and the next era of AI
Key Terms
External lab: Interact with hosted models
Graded Quiz
Knowledge check
Knowledge check
Knowledge check
Meet and Greet (optional)
27 Topics
Introduction
What is Prompt Engineering?
Zero one and few-shot prompting
Basic prompting with context
Using examples in prompts
Summary
Introduction
Setting tone and persona
Refining on previous context
Better instructions through feedback
Understanding limitations
Summary
Introduction
Limitations of context
Breaking down into smaller tasks
Using Chain of Thought
Other useful prompting techniques
Summary
Key terms
External lab: Practice Zero one and few-shot prompting
Key Terms
Strategies for better results with prompt engineering
Key Terms
Graded Quiz
Knowledge check
Knowledge check
Knowledge check
28 Topics
Introduction
Common types of Generative AI Applications
Overview of an API-based application
Overview of an embedded-model application
What is a multi-model application?
Summary
Introduction
What is RAG?
Overview of a RAG application
Managing data for RAG
Verifying embeddings and search
Using RAG with an LLM
Summary
Introduction
Application overview
Deployment overview
Setting up cloud components
Using the Azure cloud for deployment
Summary
Key Terms
Key Terms
External lab: Create a RAG with LLM using your own data
Key Terms
External lab: Create a RAG HTTP API
Graded Quiz
Knowledge check
Knowledge check
Managing data for RAG
32 Topics
Meet your Course Instructor: Derek Wales
DALL-E Overview
Demo: Environment Set Up
Demo: OpenAI API Generating a Shopping List
Demo: DALL-E to Generate an Image
OpenAI/DALL-E Summary
OpenAI Fine Tuning and Project Intro
Fine Tuning Project: Part One - Env/Data Prep
Fine Tuning Project: Part Two - Starting Fine Tuning
Fine Tuning Project: Part Three - Model Evaluation
Fine Tuning Summary
OpenAI Whisper Model Project Overview
Video Summarizer Walkthrough
Whisper Model API Wrap Up
AI Business Environment
AI Ethics Principles
Local Machine Learning Models/Next Course Preview
Module Wrap Up
Course summary
Key References
How DALL-E 2 Works
Prerequisites and Getting Started
Fine Tuning Resources
External Lab: Fine Tuning w/GPUs
Key Documentation
OpenAI Safety Best Practices
Next steps
Module Quiz
Review Questions
Review Questions
Review Questions
Practice on OpenAI/DALL-E
Introduction to Generative AI
Related Generative AI (GenAI) Courses
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