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Generative AI for Beginners
Microsoft launches free generative artificial intelligence courses for beginners
标签：AI Learning WebsiteAI learning websiteGenerative AI for Beginners is a twelve-chapter introductory course series from the Microsoft Cloud Advocates team, introducing the fundamentals of building generative AI applications. Each chapter covers a key concept in generative AI principles and application development, providing introductory videos, written materials, code examples, and recommended advanced learning resources. By completing the course, you will gain the ability to build your own generative AI products and tools, realizing your ideas using AIGC technology.
Generative AI for Beginners has added nine new video tutorials on generative AI, which delve into key practical skills such as the principles of large language models (LLM), efficient prompting engineering techniques, and image generation. The aim is to comprehensively improve learners’ professional skills and practical abilities in the field of generative AI.
Course catalog for Generative AI for Beginners
| Course Chapters | Related teaching content | Learning Objectives | |
|---|---|---|---|
| 00 | Course introduction and learning environment setup | Learning environment configuration and course structure | This course will help you achieve success. |
| 01 | Introduction to Generative Artificial Intelligence and LLMs | Key takeaways : Generative artificial intelligence and how we can adapt to the current technological landscape. | Understand what generative artificial intelligence is and how LLMs work. |
| 02 | Explore and compare different LLMs | Key concepts : Testing, iterating, and comparing different LLM models | Choose the right model for your application scenario |
| 03 | Use generative artificial intelligence responsibly | Key takeaways : Understanding the limitations of basic models and the risks behind artificial intelligence. | Learn how to responsibly build generative AI applications |
| 04 | Tips for engineering basics | Code/Knowledge Points : Best Practices for Project Development | Understand the structure and usage of prompts |
| 05 | Creating advanced tooltips and engineering techniques | Code/Knowledge Points : Expand your hinting engineering knowledge by applying different techniques in hints. | Apply prompting engineering techniques to improve prompting results |
| 06 | Create text generation applications | Code : Building a text generation application using Azure OpenAI | Learn how to effectively use tokens and temperature to change the model’s output. |
| 07 | Create a chat application | Code : Techniques for efficiently building and integrating chat applications | Identify key metrics and considerations for effectively monitoring and maintaining the quality of AI chat applications. |
| 08 | Create a search application | Code : Semantic Search vs. Keyword Search. What are text embeddings and how are they applied to search? | Create an application that uses embeddings to search data. |
| 09 | Create image generation applications | Code : Image generation and its role in building applications | Build image generation applications |
| 10 | Creating low-code AI applications | Low-code : An Introduction to Generative AI in Power Platform | Build a student assignment tracking application for our education startup using low-code. |
| 11 | Add function calling to generative AI | Code: What is Function Calling and Examples of Its Use in Applications | Configure Function Calling to retrieve data from an external API |
| 12 | Adding user experience to AI applications | Key takeaway : Designing AI applications to achieve trust and transparency | Principles of User Experience Design in Developing Generative Artificial Intelligence Applications |
| 13 | Protect your generative AI applications | Key takeaways : Understanding the threats and risks faced by AI systems, and how to protect these systems. | Master the security measures to protect generative AI applications and ensure their reliability and security. |
| 14 | Generative AI Application Lifecycle | Key takeaways : Tools and metrics for managing the LLM lifecycle and LLMops | Learn how to manage the entire lifecycle of generative AI applications, including deployment, monitoring, and optimization. |
| 15 | Retrieval Enhancement Generation (RAG) and Vector Databases | Code : Retrieving embedded applications from a vector database using the RAG framework | Learn how to combine retrieval-enhanced generative techniques to improve the performance and accuracy of generative AI applications. |
| 16 | Open source models and Hugging Face | Code : Creating an application using open-source models on Hugging Face | Master how to leverage model resources from open-source platforms like Hugging Face to quickly build generative AI applications. |
| 17 | AI Agent | Code : Creating an application using an AI agent framework | Understand the concept of AI agents and learn how to build intelligent applications using an AI agent framework. |
| 18 | Fine-tuning LLM | Key points : Fine-tuning the content, reasons, and methods of LLM. | Master how to fine-tune large language models to adapt to specific business needs. |
| 19 | Build using SLM | Key takeaway : Advantages of using small language models | Understand the characteristics and application scenarios of small language models, and how to use them to build efficient applications. |
| 20 | Build using the Mistral model | Key points : Characteristics and differences of the Mistral series models | Master the characteristics of the Mistral model and how to select the appropriate model for practical applications. |
| twenty one | Build using Meta model | Key points : Characteristics and differences of the Meta series models | Understand the advantages and application scenarios of the Meta model, and learn how to use them to build advanced applications. |
| xx | Extended Learning | Includes extended links for each chapter! | Master skills related to generative artificial intelligence |
The main functions of Generative AI for Beginners
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Basic instruction : Provides foundational knowledge of generative artificial intelligence, enabling beginners to understand the basic principles and working mechanisms.
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Model Introduction : This section introduces different generative AI models, including large language models (LLMs), and explains their applications and limitations.
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Technology Applications : Demonstrating how generative AI technologies can be applied in real-world projects, including text generation, image creation, and chatbot development.
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Practical exercises : Through hands-on experiments and project exercises, learners can personally operate generative AI tools and platforms to enhance their practical skills.
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Innovation guidance : Encourage learners to explore innovative applications of generative AI, and stimulate creative thinking and problem-solving abilities.
Application scenarios of Generative AI for Beginners
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Content creation : Generative programming helps writers, designers, and artists quickly generate creative content such as text, images, and music.
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Customer service : In the field of customer service, generative AI can create chatbots that automatically answer common questions and provide personalized services.
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Education and Training : Generative AI is used to develop personalized learning materials and simulated training environments to suit the needs of different learners.
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Game Development : In the game industry, generative AI can automatically generate game levels, storylines, and behavioral patterns of non-player characters (NPCs).
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Healthcare : Generative AI can help doctors analyze medical images, predict disease progression, and personalize treatment plans.
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