AI-For-Beginners
A 12-week, 24-lesson beginner course on AI with lessons, quizzes, and labs, using TensorFlow and PyTorch.
- Stars
- 70.5k
- Forks
- 13.7k
- Licence
- MIT
- Language
- Jupyter Notebook
- Last update
- 13 days ago
- Type
- Learning resource
What it is
AI for Beginners is a free curriculum from Microsoft that teaches artificial intelligence over 12 weeks and 24 lessons. Each lesson includes practical material, quizzes, and labs, and the course covers ethics in AI alongside the technical topics. It is hosted on GitHub so people can read it, work through the code, and contribute.
- Work through lessons on neural networks and deep learning with TensorFlow or PyTorch code
- Study neural architectures for working with images and text
- Explore less common approaches such as genetic algorithms and multi-agent systems
- Follow the course's linked mindmap and Microsoft Learn collection for further study
Great for
- Beginners who want a structured, self-paced introduction to AI
- Students who want hands-on labs alongside the concepts
- Developers who already code and want to learn neural networks with TensorFlow or PyTorch
- Learners who want a broad overview including symbolic AI, genetic algorithms, and multi-agent systems
Not ideal for
- People looking only for business strategy on AI, since the README says business cases are not covered and points to other Microsoft Learn paths
- People who want classic machine learning, since the README says that is covered in a separate ML-for-Beginners curriculum
- People who want the latest state-of-the-art models, since the README says recent models are covered but may be somewhat lacking in state-of-the-art detail
How to get it
- 1
Clone and build from source
You'll need: Git
Clone without translations (Bash / macOS / Linux)
git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.gitEnter the folder and set sparse checkout (Bash / macOS / Linux)
cd AI-For-Beginners && git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' - 2
Run it
- Clone the repository, optionally using the sparse checkout commands to reduce download size
- Open the README and pick Lesson 1 from the curriculum
- Work through each lesson's material, quiz, and lab in order
- Use the Binder badge in the README to run notebooks in the browser if you prefer not to install anything locally
- 3
Get the most out of it
- Use the Microsoft Discord or Gitter community linked in the README to ask questions
- Check the mindmap linked in the README for an overview of how topics connect
- Use the Microsoft Learn collection linked in the README for additional resources
- Use the translations listed in the README if you prefer a language other than English
Downloads
Strengths
- Free, open, and MIT-licensed, with a long track record of stars and community activity
- Covers a broad range of topics, from symbolic AI to deep learning, with quizzes and labs
- Available in 50+ translations via an automated translator
Trade-offs
- The README says it may be somewhat lacking in state-of-the-art coverage
- Business AI and classic machine learning are explicitly out of scope
- Full clone with translations is large, though the README gives a sparse checkout option
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| AI-For-Beginners (this) | 70.5k | Learning resource | beginner | 13 days ago | Beginners who want a structured, self-paced introduction to AI |
| fashion-mnist | 12.9k | Learning resource | beginner | 4.4 years ago | Machine learning students comparing classifiers on a familiar format |
| ai-engineering-from-scratch | 76.3k | Learning resource | intermediate | 4 days ago | Students who want to understand how LLMs, retrieval, and agents work internally |
| annotated_deep_learning_paper_implementations | 67.6k | Learning resource | intermediate | 4 days ago | Students learning how transformers, GANs, or optimizers work internally |
| mit-deep-learning | 10.5k | Learning resource | beginner | 2.8 years ago | Students following MIT deep learning courses |
| stanford-tensorflow-tutorials | 10.4k | Learning resource | intermediate | 5.9 years ago | Students following the original CS 20 course materials |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.