Introduction

Discover the essentials of crafting Generative AI applications through Microsoft’s Generative AI 18-lesson course for beginners. Each segment comprises a succinct video introduction, detailed written lessons available in the README, and Python and TypeScript Code Samples compatible with Azure OpenAI and OpenAI API. Additionally, access supplementary resources to further enhance your knowledge in this dynamic field.

Each lesson covers its topic, so start wherever you would like. Lessons are labeled either “Learn” lessons explaining a Generative AI concept or “Build” lessons explaining a concept and code examples in Python and TypeScript when possible. Each lesson also includes a “Keep Learning” section with additional learning tools. Further, in this blog you will get to know 18 Microsoft free courses on Gen AI

Prerequisites

• Access to the Azure OpenAI Service or OpenAI API – Only required to complete coding lessons
• Basic knowledge of Python or Typescript is helpful – For absolute beginners check out these Python and TypeScript courses.
• A Github Account to fork this entire repo to your own GitHub account

They have created a Course Setup lesson to help you with setting up your developement environment.

Setting up for the Microsoft Free Courses

Fork this Repo

Fork this entire repo to your own GitHub account to be able to change any code and complete the challenges. You can also star (🌟) this repo to find it and related repos easier.

Create a Codespace

To avoid any dependency issues when running the code, we recommend running this course in a GitHub codespace.

This can be created by selecting the Code option on your forked version of this repo and selecting the Codespaces option.

Storing Your API Keys

Keeping your API keys safe and secure is important when building any type of application. We encourage you not to store any API keys directly in the code you are working with as committing those details to a public repository could result in unwanted costs and issues.

How to Run locally on your computer

To run the code locally on your computer, you would need to have some version of Python installed.

To then use the repository, you need to clone it:

git clone https://github.com/microsoft/generative-ai-for-beginners
cd generative-ai-for-beginners

Now you have everything checked out and can start learning and work with the code.

Microsoft Free Courses for Gen AI Learning

Here are the Microsoft Free Courses on Gen AI you must know:

Course 1: Introduction to Generative AI and LLMs

Link to Access Course

This lesson will cover:

• Introduction to the business scenario: our startup idea and mission.
• Generative AI and how we landed on the current technology landscape.
• Inner working of a large language model.
• Main capabilities and practical use cases of Large Language Models.

Learning Goals

After completing this lesson, you will understand:

• What generative AI is and how Large Language Models work.
• How you can leverage large language models for different use cases, with a focus on education scenarios.

Course 2: Exploring and comparing different LLMs

Link to Access Course

This lesson will cover:

• Different types of LLMs in the current landscape.
• Testing, iterating, and comparing different models for your use case in Azure.
• How to deploy an LLM.

Learning Goals

After completing this lesson, you will be able to:

• Select the right model for your use case.
• Understand how to test, iterate, and improve performance of your model.
• Know how businesses deploy models.

Course 3: Using Generative AI Responsibly

Link to Access Course

This lesson will cover:

• Why you should prioritize Responsible AI when building Generative AI applications.
• Core principles of Responsible AI and how they relate to Generative AI.
• How to put these Responsible AI principles into practice through strategy and tooling.

Learning Goals

After completing this lesson you will know:

• The importance of Responsible AI when building Generative AI applications.
• When to think and apply the core principles of Responsible AI when building Generative AI applications.
• What tools and strategies are available to you to put the concept of Responsible AI into practice.

Course 4: Understanding Prompt Engineering Fundamentals

Link to Access Course

In this lesson, we learn what Prompt Engineering is, why it matters, and how we can craft more effective prompts for a given model and application objective. We’ll understand core concepts and best practices for prompt engineering – and learn about an interactive Jupyter Notebooks “sandbox” environment where we can see these concepts applied to real examples.

By the end of this lesson we will be able to:

1. Explain what prompt engineering is and why it matters.
2. Describe the components of a prompt and how they are used.
3. Learn best practices and techniques for prompt engineering.
4. Apply learned techniques to real examples, using an OpenAI endpoint.

Course 5: Creating Advanced Prompts

Link to Access Course

In this chapter, we will cover the following topics:

• Extend your knowledge of prompt engineering by applying different techniques to your prompts.
• Configuring your prompts to vary the output.

Learning Goals

After completing this lesson, you’ll be able to:

• Apply prompt engineering techniques that improve the outcome of your prompts.
• Perform prompting that is either varied or deterministic.

Course 6: Building Text Generation Applications

Link to Access Course

In this chapter, you will:

• Learn about the openai library and it’s core concepts.
• Build a text generation app using openai.
• Understand how to use concepts like prompt, temperature, and tokens to build a text generation app.

Learning Goals

At the end of this lesson, you’ll be able to:

• Explain what a text generation app is.
• Build a text generation app using openai.
• Configure your app to use more or less tokens and also change the temperature, for a varied output.

Course 7: Building Chat Applications

Link to Access Course

This lesson covers:

• Techniques for efficiently building and integrating chat applications.
• How to apply customization and fine-tuning to applications.
• Strategies and considerations to effectively monitor chat applications.

Learning Goals

By the end of this lesson, you’ll be able to:

• Describe considerations for building and integrating chat applications into existing systems.
• Customize chat applications for specific use-cases.
• Identify key metrics and considerations to monitor and maintain the quality of AI-powered chat applications effectively.
• Ensure chat applications leverage AI responsibly.

Course 8: Building Search Apps Vector Databases

Link to Access Course

In this lesson, we will cover:

• Semantic vs Keyword search.
• What are Text Embeddings.
• Creating a Text Embeddings Index.
• Searching a Text Embeddings Index.

Learning Goals

After completing this lesson, you will be able to:

• Tell the difference between semantic and keyword search.
• Explain what Text Embeddings are.
• Create an application using Embeddings to search for data.

Course 9: Building Image Generation Applications

Link to Access Course

In this lesson, we will cover:

• Image generation and why it’s useful.
• DALL-E and Midjourney, what they are, and how they work.
• How you would build an image generation app.

Learning Goals

After completing this lesson, you will be able to:

• Build an image generation application.
• Define boundaries for your application with meta prompts.
• Work with DALL-E and Midjourney.

Course 10: Building Low Code AI Applications

Link to Access Course

This lesson covers:

• Introduction to Generative AI in Power Platform
• Introduction to Copilot and how to use it
• Using Generative AI to build apps and flows in Power Platform
• Understanding the AI Models in Power Platform with AI Builder

Learning Goals

By the end of this lesson, you will be able to:

• Understand how Copilot works in Power Platform.
• Build a Student Assignment Tracker App for our education startup.
• Build an Invoice Processing Flow that uses AI to extract information from invoices.
• Apply best practices when using the Create Text with GPT AI Model.

Course 11: Integrating External Applications with Function Calling

Link to Access Course

This lesson will cover:

• Explain what is function calling and its use cases.
• Creating a function call using Azure OpenAI.
• How to integrate a function call into an application.

Learning Goals

After completing this lesson you will be able to:

• Explain the purpose of using function calling.
• Setup Function Call using the Azure Open AI Service.
• Design effective function calls for your application’s use case.

Course 12: Designing UX for AI Applications

Link to Access Course

The lesson will cover the following areas:

• Introduction to User Experience and Understanding User Needs
• Designing AI Applications for Trust and Transparency
• Designing AI Applications for Collaboration and Feedback

Learning Goals

After taking this lesson, you’ll be able to:

• Understand how to build AI applications that meet the user needs.
• Design AI applications that promote trust and collaboration.

Course 13: Securing Your Generative AI Applications

Link to Access Course

This lesson will cover:

• Security within the context of AI systems.
• Common risks and threats to AI systems.
• Methods and considerations for securing AI systems.

Learning Goals

After completing this lesson, you will have an understanding of:

• The threats and risks to AI systems.
• Common methods and practices for securing AI systems.
• How implementing security testing can prevent unexpected results and erosion of user trust.

Course 14: The Generative AI Application Lifecycle

Link to Access Course

In this chapter, you will:

• Understand the Paradigm Shift from MLOps to LLMOps
• The LLM Lifecycle
• Lifecycle Tooling
• Lifecycle Metrification and Evaluation

Course 15: Retrieval Augmented Generation (RAG) and Vector Databases

Link to Access Course

In this lesson we will cover the following:

• An introduction to RAG, what it is and why it is used in AI (artificial intelligence).
• Understanding what vector databases are and creating one for our application.
• A practical example on how to integrate RAG into an application.

Learning Goals

After completing this lesson, you will be able to:

• Explain the significance of RAG in data retrieval and processing.
• Setup RAG application and ground your data to an LLM
• Effective integration of RAG and Vector Databases in LLM Applications.
  

Course 16: Open Source Models and Hugging Face

Link to Access Course

Learning Goals

• Gain an understanding of open source Models
• Understanding the benefits of working with open source Models
• Exploring the open models available on Hugging Face and the Azure AI Studio

Course 17: AI Agents

Link to Access Course

AI Agents represent an exciting development in Generative AI, enabling Large Language Models (LLMs) to evolve from assistants into agents capable of taking actions. AI Agent frameworks enable developers to create applications that give LLMs access to tools and state management. These frameworks also enhance visibility, allowing users and developers to monitor the actions planned by LLMs, thereby improving experience management.

The lesson will cover the following areas:

• Understanding what an AI Agent is – What exactly is an AI Agent?
• Exploring four different AI Agent Frameworks – What makes them unique?
• Applying these AI Agents to different use cases – When should we use AI Agents?

Learning Goals

After taking this lesson, you’ll be able to:

• Explain what AI Agents are and how they can be used.
• Have an understanding of the differences between some of the popular AI Agent Frameworks, and how they differ.
• Understand how AI Agents function in order to build applications with them.

Course 18: Fine-Tuning LLMs

Link to Access Course

This lesson introduces the concept of fine-tuning for pre-trained language models, explores the benefits and challenges of this approach, and provides guidance on when and how to use fine tuning to improve the performance of your generative AI models.

Learning Goals

• What is fine tuning for language models?
• When, and why, is fine tuning useful?
• How can I fine-tune a pre-trained model?
• What are the limitations of fine-tuning?

After completing this lesson, check out our Generative AI Learning collection to continue leveling up your Generative AI knowledge! Congratsulations!! You have completed the final lesson from the v2 series for this course! Don’t stop learning and building. Check out the Resources page for a list of additional suggestions for just this topic.

Conclusion

The given Microsoft Free Courses on Generative AI marks a significant stride towards democratizing access to cutting-edge technology education. These courses promise to empower learners with the foundational knowledge and practical skills needed to delve into the realm of AI-driven creativity. By providing these Microsoft free courses, Microsoft demonstrates its commitment to fostering innovation and equipping individuals worldwide with the tools to harness the potential of Generative AI for diverse applications.

Other than Microsoft Free Courses, you can unlock your potential with the GenAI Pinnacle Program! Elevate your AI expertise through revolutionary learning and development. Experience personalized 1:1 mentorship with industry-leading Generative AI experts, dive deep into an advanced curriculum featuring over 200 hours of immersive learning, and master 26+ cutting-edge GenAI tools and libraries. Don’t just learn AI, pioneer its future with GenAI Pinnacle!