Open Source Tutorial Highly Recommended: “AI For Beginners” — by Microsoft: 12 Weeks, 24 Lessons to Learn AI from Scratch, Covering Multi-Agent Systems, Reinforcement Learning, and AI Ethics

Want to systematically learn AI but don’t know where to start? Microsoft has open-sourced a complete AI university curriculum: 12 weeks, 24 lessons, using Jupyter Notebook with hands-on labs, from neural network basics all the way to multi-agent systems.

This project isn’t just theory — each lesson includes quizzes, lab experiments, and projects. TensorFlow + PyTorch dual frameworks run in parallel, with a dedicated module on AI ethics and responsible AI.

Core Features:

1. 12-Week Systematic Curriculum: From AI fundamentals to deep learning, NLP, reinforcement learning, and multi-agent systems.
2. Each Lesson Includes Quizzes + Labs: Hands-on experiments paired with theory, with runnable code.
3. Dual Track: TensorFlow + PyTorch: Framework-agnostic; choose after learning.
4. Dedicated Multi-Agent Systems Chapter: Complete coverage of genetic algorithms, reinforcement learning, and multi-agent systems.
5. AI Ethics Module: Built-in responsible AI thinking framework that doesn’t shy away from real-world issues.
6. Multi-Language Community Versions: Maintained by a global developer community with multi-language translations.

Especially suitable for engineers and developers who are just starting AI, or who already use AI tools but want to fill in the foundational theory. Currently at 50.7k+ stars, it’s the most popular AI educational resource on Microsoft GitHub, with years of continuous updates and maintenance.

Complementary difference from existing AI tutorial resources: Most AI courses are either too shallow (ChatGPT prompt engineering introduction) or too deep (paper-level mathematical derivations). AI-For-Beginners positions itself at the engineering practice layer — runnable code, explainable concepts, and testable knowledge points. For developers already using AI tools like Claude Code, this is one of the best starting points to fill in the underlying logic of “why AI works this way.”

https://github.com/microsoft/AI-For-Beginners…

#AITutorial #LLMFromScratch #AIEngineering #AIProgramming #MicrosoftOpenSource

microsoft/AI-For-Beginners

Source: https://github.com/microsoft/AI-For-Beginners

GitHub license (https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE) GitHub contributors (https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) GitHub issues (https://GitHub.com/microsoft/AI-For-Beginners/issues/) GitHub pull-requests (https://GitHub.com/microsoft/AI-For-Beginners/pulls/) PRs Welcome (http://makeapullrequest.com)

GitHub watchers (https://GitHub.com/microsoft/AI-For-Beginners/watchers/) GitHub forks (https://GitHub.com/microsoft/AI-For-Beginners/network/) GitHub stars (https://GitHub.com/microsoft/AI-For-Beginners/stargazers/) Binder (https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD) Gitter (https://gitter.im/Microsoft/ai-for-beginners?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)

Microsoft Foundry Discord (https://discord.gg/nTYy5BXMWG)

Artificial Intelligence for Beginners - A Curriculum

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 (https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)

Join the Community

Microsoft Foundry Discord (https://discord.gg/nTYy5BXMWG)

What you will learn

Mindmap of the Course (http://soshnikov.com/courses/ai-for-beginners/mindmap.html)

In this curriculum, you will learn:

• Different approaches to Artificial Intelligence, including the “good old” symbolic approach with Knowledge Representation and reasoning (GOFAI (https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)).
• 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 (http://Tensorflow.org) and PyTorch (http://pytorch.org).
• 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 (https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)

• Business cases of using AI in Business. Consider taking Introduction to AI for business users (https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) learning path on Microsoft Learn, or AI Business School (https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), developed in cooperation with INSEAD (https://www.insead.edu/).
• Classic Machine Learning, which is well described in our Machine Learning for Beginners Curriculum (http://github.com/Microsoft/ML-for-Beginners).
• Practical AI applications built using Cognitive Services (https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum). For this, we recommend that you start with modules Microsoft Learn for vision (https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), natural language processing (https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), Generative AI with Azure OpenAI Service (https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum) and others.
• Specific ML Cloud Frameworks, such as Azure Machine Learning (https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), Microsoft Fabric (https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), or Azure Databricks (https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Consider using Build and operate machine learning solutions with Azure Machine Learning (https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) and Build and Operate Machine Learning Solutions with Azure Databricks (https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) learning paths.
• Conversational AI and Chat Bots. There is a separate Create conversational AI solutions (https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) learning path, and you can also refer to this blog post (https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) for more detail.
• Deep Mathematics behind deep learning. For this, we would recommend Deep Learning (https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) by Ian Goodfellow, Yoshua Bengio and Aaron Courville, which is also available online at https://www.deeplearningbook.org/.

For a gentle introduction to AI in the Cloud topics you may consider taking the Get started with artificial intelligence on Azure (https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) Learning Path.

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