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microsoft AI-For-Beginners
12 Weeks, 24 Lessons, AI for All!
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License: MIT License
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| 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
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:
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):
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
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
- Business cases of using AI in Business. Consider taking Introduction to AI for business users learning path on Microsoft Learn, or AI Business School, developed in cooperation with INSEAD.
- Classic Machine Learning, which is well described in our Machine Learning for Beginners Curriculum.
- Practical AI applications built using Cognitive Services. For this, we recommend that you start with modules Microsoft Learn for vision, natural language processing, Generative AI with Azure OpenAI Service and others.
- Specific ML Cloud Frameworks, such as Azure Machine Learning, Microsoft Fabric, or Azure Databricks. Consider using Build and operate machine learning solutions with Azure Machine Learning and Build and Operate Machine Learning Solutions with Azure Databricks learning paths.
- Conversational AI and Chat Bots. There is a separate Create conversational AI solutions learning path, and you can also refer to this blog post for more detail.
- Deep Mathematics behind deep learning. For this, we would recommend Deep Learning 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 Learning Path.
- 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.
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.
- We have created a setup lesson to help you with setting up your development environment. - For Educators, we have created a curricula setup lesson for you too!
- How to Run the code in a VSCode or a Codespace
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.
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
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-appfolder. They are gradually being localized.
Do you have suggestions or found spelling or code errors? Raise an issue or create a pull request.
- ✍️ Primary Author: Dmitry Soshnikov, PhD
- 🔥 Editor: Jen Looper, PhD
- 🎨 Sketchnote illustrator: Tomomi Imura
- ✅ Quiz Creator: Lateefah Bello, MLSA
- 🙏 Core Contributors: Evgenii Pishchik
Our team produces other curricula! Check out:
ML for Beginners
Data Science for Beginners
AI for Beginners
Cybersecurity for Beginners
Web Dev for Beginners
IoT for Beginners
XR Development for Beginners
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.
If you have product feedback or errors while building visit:
Repository Radar analysis
Deterministic insights derived from public metadata and our observations — not personal testing or reviews.
Why this repository is interesting
- Listed in our discovery index with public GitHub metadata for analysis.
Who should use it
- Developers working primarily with Jupyter Notebook
- Teams exploring AI tooling, agents, or ML infrastructure
- Platform and DevOps engineers
Potential use cases
- Reference or evaluate Jupyter Notebook open-source approaches in this domain
- Prototype AI/agent workflows or study reference architectures
- Evaluate automation, CI/CD, or infrastructure patterns
Strengths
- README present in our index
- Declared license: MIT License
- Substantial fork count (13,118) suggests reuse and contribution interest
Limitations / considerations
- Insights are derived from public metadata and our observations — not a substitute for code review
What to watch
- Re-check last push, issues, and releases on GitHub before production adoption
Source: GitHub (public metadata) + Repository Radar analysis. We do not claim ownership of third-party repositories.
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