AI-For-Beginners

microsoft / AI-For-Beginners

微软出品的 AI 入门免费课程,12 周 24 课时,覆盖神经网络、NLP、计算机视觉等核心主题,适合零基础开发者系统学习。

Jupyter Notebook 数据科学 模型训练 开发工具 AI 入门 机器学习 深度学习 免费课程

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微软官方维护,内容结构清晰,每课配有 Jupyter Notebook 实战练习,理论与实践结合,非常适合想入门 AI 的开发者。课程覆盖面广但深度有限,适合作为学习路线图,后续需结合其他资源深入。全部免费开源,社区活跃,是极佳的自学起点。

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Artificial Intelligence for Beginners - A Curriculum

Sketchnote by @girlie_mac https://twitter.com/girlie_mac
AI For Beginners - Sketchnote by @girlie_mac

Explore the world of Artificial Intelligence (AI) with our 12-week, 24-lesson curriculum! It includes practical lessons, quizzes, and labs. The curriculum is beginner-friendly and covers tools like TensorFlow and PyTorch, as well as ethics in AI

🌐 Multi-Language Support

Supported via GitHub Action (Automated & Always Up-to-Date)

Arabic | Bengali | Bulgarian | Burmese (Myanmar) | Chinese (Simplified) | Chinese (Traditional, Hong Kong) | Chinese (Traditional, Macau) | Chinese (Traditional, Taiwan) | Croatian | Czech | Danish | Dutch | Estonian | Finnish | French | German | Greek | Hebrew | Hindi | Hungarian | Indonesian | Italian | Japanese | Kannada | Khmer | Korean | Lithuanian | Malay | Malayalam | Marathi | Nepali | Nigerian Pidgin | Norwegian | Persian (Farsi) | Polish | Portuguese (Brazil) | Portuguese (Portugal) | Punjabi (Gurmukhi) | Romanian | Russian | Serbian (Cyrillic) | Slovak | Slovenian | Spanish | Swahili | Swedish | Tagalog (Filipino) | Tamil | Telugu | Thai | Turkish | Ukrainian | Urdu | Vietnamese

Prefer to Clone Locally?

This repository includes 50+ language translations which significantly increases the download size. To clone without translations, use sparse checkout:

Bash / macOS / Linux: bash git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git cd AI-For-Beginners git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'

CMD (Windows): cmd git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git cd AI-For-Beginners git sparse-checkout set --no-cone "/*" "!translations" "!translated_images"

This gives you everything you need to complete the course with a much faster download.

If you wish to have additional translations languages supported are listed here

Join the Community

Microsoft Foundry Discord

🤝 Contributing

We welcome contributions from the community! Whether you're fixing typos, improving documentation, or adding new examples, your help makes this curriculum better for everyone. Check out our CONTRIBUTING.md guide to get started.

What you will learn

Mindmap of the Course

In this curriculum, you will learn:

  • Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI).
  • Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch.
  • Neural Architectures for working with images and text. We will cover recent models but may be a bit lacking in the state-of-the-art.
  • Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.

What we will not cover in this curriculum:

Find all additional resources for this course in our Microsoft Learn collection

For a gentle introduction to AI in the Cloud topics you may consider taking the Get started with artificial intelligence on Azure Learning Path.

Content

Lesson Link PyTorch/Keras/TensorFlow Lab
0 Course Setup Setup Your Development Environment
I Introduction to AI
01 Introduction and History of AI - -
II Symbolic AI
02 Knowledge Representation and Expert Systems Expert Systems / Ontology /Concept Graph
III Introduction to Neural Networks
03 Perceptron Notebook Lab
04 Multi-Layered Perceptron and Creating our own Framework Notebook Lab
05 Intro to Frameworks (PyTorch/TensorFlow) and Overfitting PyTorch / Keras / TensorFlow Lab
IV Computer Vision PyTorch / TensorFlow Explore Computer Vision on Microsoft Azure
06 Intro to Computer Vision. OpenCV Notebook Lab
07 Convolutional Neural Networks & CNN Architectures PyTorch /TensorFlow Lab
08 Pre-trained Networks and Transfer Learning and Training Tricks PyTorch / TensorFlow Lab
09 Autoencoders and VAEs PyTorch / TensorFlow
10 Generative Adversarial Networks & Artistic Style Transfer PyTorch / TensorFlow
11 Object Detection TensorFlow Lab
12 Semantic Segmentation. U-Net PyTorch / TensorFlow
V Natural Language Processing PyTorch /TensorFlow Explore Natural Language Processing on Microsoft Azure
13 Text Representation. Bow/TF-IDF PyTorch / TensorFlow
14 Semantic word embeddings. Word2Vec and GloVe PyTorch / TensorFlow
15 Language Modeling. Training your own embeddings PyTorch / TensorFlow Lab
16 Recurrent Neural Networks PyTorch / TensorFlow
17 Generative Recurrent Networks PyTorch / TensorFlow Lab
18 Transformers. BERT. PyTorch /TensorFlow
19 Named Entity Recognition TensorFlow Lab
20 Large Language Models, Prompt Programming and Few-Shot Tasks PyTorch
VI Other AI Techniques
21 Genetic Algorithms Notebook
22 Deep Reinforcement Learning PyTorch /TensorFlow Lab
23 Multi-Agent Systems
VII AI Ethics
24 AI Ethics and Responsible AI Microsoft Learn: Responsible AI Principles
IX Extras
25 Multi-Modal Networks, CLIP and VQGAN Notebook

Each lesson contains

  • Pre-reading material
  • Executable Jupyter Notebooks, which are often specific to the framework (PyTorch or TensorFlow). The executable notebook also contains a lot of theoretical material, so to understand the topic you need to go through at least one version of the notebook (either PyTorch or TensorFlow).
  • Labs available for some topics, which give you an opportunity to try applying the material you have learned to a specific problem.
  • Some sections contain links to MS Learn modules that cover related topics.

Getting Started

🎯 New to AI? Start Here!

If you're completely new to AI and want quick, hands-on examples, check out our Beginner-Friendly Examples! These include:

  • 🌟 Hello AI World - Your first AI program (pattern recognition)
  • 🧠 Simple Neural Network - Build a neural network from scratch
  • 🖼️ Image Classifier - Classify images with detailed comments
  • 💬 Text Sentiment - Analyze positive/negative text

These examples are designed to help you understand AI concepts before diving into the full curriculum.

📚 Full Curriculum Setup

Follow these steps:

Fork the Repository: Click on the "Fork" button at the top-right corner of this page.

Clone the Repository: git clone https://github.com/microsoft/AI-For-Beginners.git

Don't forget to star (🌟) this repo to find it easier later.

Meet other Learners

Join our official AI Discord server to meet and network with other learners taking this course and get support.

If you have product feedback or questions whilst building visit our Azure AI Foundry Developer Forum

Quizzes

A note about quizzes: All quizzes are contained in the Quiz-app folder in etc\quiz-app, or Online Here They are linked from within the lessons the quiz app can be run locally or deployed to Azure; follow the instruction in the quiz-app folder. They are gradually being localized.

Help Wanted

Do you have suggestions or found spelling or code errors? Raise an issue or create a pull request.

Special Thanks

Other Curricula

Our team produces other curricula! Check out:

LangChain


Azure / Edge / MCP / Agents


Generative AI Series

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Core Learning


Copilot Series

Getting Help

If you get stuck or have any questions about building AI apps. Join fellow learners and experienced developers in discussions about MCP. It's a supportive community where questions are welcome and knowledge is shared freely.

Microsoft Foundry Discord

If you have product feedback or errors while building visit:

文档抓取自 GitHub 仓库 README,版权归原作者所有;已过滤徽章等噪音并经安全消毒后展示。