Microsoft Generative AI Course for Beginners

Microsoft's Generative AI Course for Beginners is a comprehensive program designed to introduce learners to the fundamentals of generative AI. The course covers a wide range of topics, including the basics of AI, machine learning, and deep learning.

The course is structured to be self-paced, allowing learners to progress at their own speed. This flexibility is a major advantage, especially for those with busy schedules or learning at their own pace.

One of the key features of the course is its focus on practical applications of generative AI. Learners will gain hands-on experience with tools and techniques, such as text-to-image synthesis and music generation.

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To get the most out of this course, you'll need to meet some basic prerequisites.

You'll need access to the Azure OpenAI Service or OpenAI API, but only to complete the coding lessons. Basic knowledge of Python or Typescript is also helpful, and if you're an absolute beginner, you can check out some Python and TypeScript courses.

You'll also need a Github Account to fork the entire repo to your own GitHub account.

Here are the specific requirements in a concise list:

After completing the Microsoft Generative AI course, you'll understand what generative AI is and how Large Language Models work. You'll learn how to leverage large language models for different use cases, with a focus on education scenarios.

You'll be able to define generative AI and illustrate how insights derived from supervised learning have enhanced our comprehension of Generative AI.

Generative AI can be applied to various tasks, including writing, reading, and chatting on web-based and software-based interfaces. You'll learn common use cases for Generative AI and practical techniques for creating prompts that enhance the quality and relevance of large language model responses.

Here are the key learning objectives of the course:

In this Microsoft Generative AI Course, you'll learn about various applications and use cases of generative AI. You can explore generative AI with Copilot in Bing, which enables learners to perform use case exercises in business-related scenarios, including executives, sales, marketing, finance, IT, HR, and operations.

The course covers a range of generative AI applications, including video-based lessons on writing, reading, chatting, and image generation. You'll also learn about the lifecycle of a generative AI project and how to estimate costs.

Here are some specific generative AI applications you can expect to learn about:

Additionally, you'll have the opportunity to try out generative AI code yourself and explore real-world prompts for specific use case scenarios.

Comparing Different Models is a crucial step in finding the right Large Language Model (LLM) for your needs. You'll want to select the right model for your use case.

There are different types of LLMs in the current landscape, each with their own strengths and weaknesses. You'll need to research and compare these models to make an informed decision.

To get started, you can test, iterate, and compare different models for your use case in Azure. This will help you understand how each model performs and which one is best suited for your needs.

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Here are some key things to consider when comparing different models:

By considering these factors, you'll be able to make an informed decision and choose the best LLM for your needs.

Image generation applications are a fascinating area of generative AI. They allow us to create new images based on text prompts, which can be incredibly useful.

DALL-E and Midjourney are two popular tools that can generate images based on text prompts. DALL-E is a model that uses a combination of natural language processing and computer vision to generate images, while Midjourney is a text-to-image model that uses a different approach to generate images.

To build an image generation application, you can start by defining boundaries for your application with meta prompts. This will help you control the output of your application and ensure that it produces the desired results.

Here are some key features of image generation applications:

These features can help you create a robust and effective image generation application that meets your needs.

Low code applications are revolutionizing the way we build software, making it faster and more accessible to non-technical users.

You can use Power Platform to build low code AI applications, such as a Student Assignment Tracker App, which can be created using Generative AI.

Generative AI in Power Platform can be used to build apps and flows, and it's essential to understand how Copilot works in Power Platform to get the most out of it.

To apply best practices when using the Create Text with GPT AI Model, you should follow the guidelines provided in the course materials.

Here's a summary of the low code applications you can build with Power Platform:

Building these applications requires a good understanding of Generative AI and its capabilities, which is covered in the course materials.

Designing UX is a crucial aspect of creating effective AI applications. It's about understanding user needs and building applications that meet those needs.

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To design UX for AI applications, you need to focus on several key areas. One of these areas is introducing users to the basics of user experience and understanding their needs.

Designing AI applications for trust and transparency is also essential. This involves creating applications that are transparent in their decision-making processes and trustworthy in their interactions with users.

Designing AI applications for collaboration and feedback is another critical area. This involves creating applications that allow users to collaborate and provide feedback, promoting a more interactive and engaging experience.

After taking a lesson on designing UX for AI applications, you'll be able to understand how to build AI applications that meet user needs. You'll also be able to design AI applications that promote trust and collaboration.

Here are the key takeaways from designing UX for AI applications:

Advanced technologies like Retrieval Augmented Generation (RAG) and fine-tuning can take your language model to the next level.

In Lesson 2, you'll learn about these cutting-edge techniques through interactive videos and a quiz.

Video tutorials on fine-tuning, pretraining an LLM, and choosing a model are also part of this lesson.

These videos will give you hands-on experience with advanced technologies.

You'll also have the option to learn about instruction tuning and RLHF, as well as tool use and agents.

Here are some key technologies you'll explore in this lesson:

You should prioritize Responsible AI when building Generative AI applications because it's essential for creating trustworthy and beneficial technology.

The course highlights three key principles of Responsible AI that are relevant to Generative AI: prioritizing transparency, ensuring fairness, and preventing harm.

These principles are crucial when building Generative AI applications because they help prevent the creation of biased or discriminatory models.

To put these principles into practice, you can use various tools and strategies, such as data validation and model auditing.

Here are some key takeaways to keep in mind:

The Microsoft Generative AI course is structured to take you from the basics of Generative AI to advanced application development. The course is divided into 12 lessons, each with a specific focus.

Here's a quick overview of the lessons in the course:

By the end of the course, you'll be well-equipped to launch your own AI projects.

Week 2 is where the rubber meets the road, and you'll get hands-on experience with Generative AI projects. You'll identify and build generative AI use cases and technology options.

The course is structured to take you from the basics to advanced application development, with 12 lessons that cover everything from introduction to Generative AI and LLMs to designing UX for AI applications.

Lesson 1 sets the stage with an introduction to Generative AI, which includes a video on how Generative AI works and a quiz to test your understanding. By the end of the course, you'll be well-equipped to launch your own AI projects.

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Here's a quick overview of the lessons in the course:

By the time you reach Week 2, you'll have a solid foundation in Generative AI and be ready to dive into real-world projects.

In Week 3 of the course, you'll explore the impact of Generative AI on business and society. This includes understanding how teams can take advantage of Generative AI and learning about AI risks and responsible AI practices.

The course covers different aspects of Generative AI, from its applications in software development to its potential impact on various industries. You'll learn how to identify and build Generative AI use cases and technology options, as mentioned in Week 2.

Generative AI has the potential to revolutionize various sectors, including education, healthcare, and finance. By the end of the course, you'll be well-equipped to launch your own AI projects.

Here's a breakdown of the topics covered in Week 3:

By understanding these aspects, you'll be able to harness the power of Generative AI and make informed decisions about its implementation in your work and personal life.