Microsoft Offers an excellent course on learning AI for beginners. I am particularly impressed by the quality of the explainations and the videos.
This is a comprehensive 18-lesson course on Generative AI for Beginners by Microsoft Cloud Advocates. The course teaches fundamental concepts of building Generative AI applications with practical examples in both Python and TypeScript.
Key Features:
18 Lessons: Each lesson covers a specific topic on Generative AI, from introductory concepts to advanced applications. “Learn” and “Build” Lessons: Lessons are categorized into “Learn” lessons explaining concepts and “Build” lessons providing code examples and practical exercises. Open Source and Accessible: The course materials are open source and readily available on GitHub, allowing users to fork the repository and follow along. Practical Learning: Lessons include videos, written explanations, code samples, and additional learning resources to enhance understanding and application. Community Support: Users can join a dedicated Discord server for interaction, support, and networking with other learners. Free OpenAI Credits: Through Microsoft for Startups Founders Hub, participants can receive free OpenAI credits and Azure credits to access OpenAI models. Topics Covered:
Introduction to Generative AI and LLMs Exploring and comparing different LLMs Using Generative AI responsibly Prompt Engineering Fundamentals Creating Advanced Prompts Building Text Generation Applications Building Chat Applications Building Search Applications using Vector Databases Building Image Generation Applications Building Low Code AI Applications Integrating External Applications with Function Calling Designing UX for AI Applications Securing Generative AI Applications The Generative AI Application Lifecycle Retrieval Augmented Generation (RAG) and Vector Databases Open Source Models and Hugging Face AI Agents Fine-Tuning LLMs Benefits:
Learn the basics of Generative AI and how to build applications. Gain hands-on experience with code examples and practical exercises. Develop a strong understanding of LLMs and their applications. Acquire knowledge of responsible AI development and ethical considerations. Access free resources and credits to support your learning and project development. Connect with a community of other learners and get support.
Learn the fundamentals of building Generative AI applications with our 18-lesson comprehensive course by Microsoft Cloud Advocates.
This course has 18 lessons. Each lesson covers its own topic so start wherever you like!
Lessons are labeled either “Learn” lessons explaining a Generative AI concept or “Build” lessons that explain a concept and code examples in both Python and TypeScript when possible.
Each lesson also includes a “Keep Learning” section with additional learning tools.
What You Need
We have created a Course Setup lesson to help you with setting up your development environment.
Don’t forget to star (🌟) this repo to find it easier later.
If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both Python and TypeScript.
Join our official AI Discord server to meet and network with other learners taking this course and get support.
Sign up for Microsoft for Startups Founders Hub to receive free OpenAI credits and up to $150k towards Azure credits to access OpenAI models through Azure OpenAI Services.
Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request
# | Lesson Link | Description | Video | Extra Learning |
---|---|---|---|---|
00 | Course Setup | Learn: How to Setup Your Development Environment | Coming Soon | Learn More |
01 | Introduction to Generative AI and LLMs | Learn: Understanding what Generative AI is and how Large Language Models (LLMs) work. | Video | Learn More |
02 | Exploring and comparing different LLMs | Learn: How to select the right model for your use case | Video | Learn More |
03 | Using Generative AI Responsibly | Learn: How to build Generative AI Applications responsibly | Video | Learn More |
04 | Understanding Prompt Engineering Fundamentals | Learn: Hands-on Prompt Engineering Best Practices | Video | Learn More |
05 | Creating Advanced Prompts | Learn: How to apply prompt engineering techniques that improve the outcome of your prompts. | Video | Learn More |
06 | Building Text Generation Applications | Build: A text generation app using Azure OpenAI / OpenAI API | Video | Learn More |
07 | Building Chat Applications | Build: Techniques for efficiently building and integrating chat applications. | Video | Learn More |
08 | Building Search Apps Vector Databases | Build: A search application that uses Embeddings to search for data. | Video | Learn More |
09 | Building Image Generation Applications | Build: A image generation application | Video | Learn More |
10 | Building Low Code AI Applications | Build: A Generative AI application using Low Code tools | Video | Learn More |
11 | Integrating External Applications with Function Calling | Build: What is function calling and its use cases for applications | Video | Learn More |
12 | Designing UX for AI Applications | Learn: How to apply UX design principles when developing Generative AI Applications | Video | Learn More |
13 | Securing Your Generative AI Applications | Learn: The threats and risks to AI systems and methods to secure these systems. | Video | Learn More |
14 | The Generative AI Application Lifecycle | Learn: The tools and metrics to manage the LLM Lifecycle and LLMOps | Video | Learn More |
15 | Retrieval Augmented Generation (RAG) and Vector Databases | Build: An application using a RAG Framework to retrieve embeddings from a Vector Databases | Video | Learn More |
16 | Open Source Models and Hugging Face | Build: An application using open source models available on Hugging Face | Video | Learn More |
17 | AI Agents | Build: An application using an AI Agent Framework | Video | Learn More |
18 | Fine-Tuning LLMs | Learn: The what, why and how of fine-tuning LLMs | Video | Learn More |
Special thanks to John Aziz for creating all of the GitHub Actions and workflows
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