Last updated: 14 October 2026
Microsoft gives away some of the best free AI courses on the internet, and most people have never heard of them. The most popular, AI for Beginners, is a full 12-week course on GitHub where you build your own neural network from scratch before moving on to computer vision, transformers, reinforcement learning and AI ethics. It is completely free and open source.
This guide explains Microsoft’s three free “for Beginners” AI courses, what you will learn in each, who they suit, how to start in about 10 minutes, and the important question many people ask: do you get a certificate?
Quick Answer: Microsoft’s Free AI Courses at a Glance
| Course | Length | Best for | Certificate? |
|---|---|---|---|
| AI for Beginners | 12 weeks, 24 lessons | Learning how AI and neural networks really work | No |
| ML for Beginners | 12 weeks, 26 lessons, 52 quizzes | Classic machine learning with Python and Scikit-learn | No |
| Generative AI for Beginners | 21 lessons | Building apps with large language models (LLMs) | No |
| Microsoft Learn / Applied Skills | Short modules and lab assessments | Earning a free credential to show employers | Badges and free Applied Skills credentials |
All three “for Beginners” courses are free and released under the open-source MIT licence. They do not come with a certificate, but you can pair them with free Microsoft Learn credentials (explained below).
1. AI for Beginners (12 Weeks, 24 Lessons)
AI for Beginners is Microsoft’s flagship free AI curriculum, available on GitHub at github.com/microsoft/AI-For-Beginners. It has over 60,000 stars on GitHub and has been translated into more than 50 languages.
What you will learn
- Introduction to AI: the history of AI and the main approaches
- Symbolic AI: knowledge representation and expert systems
- Neural networks: perceptrons and multi-layer networks, including building your own neural network framework from scratch before using any library
- Computer vision: OpenCV, convolutional neural networks (CNNs), transfer learning, autoencoders, GANs, object detection and image segmentation
- Natural language processing: word embeddings, recurrent neural networks, transformers, BERT and large language models
- Other techniques: genetic algorithms, deep reinforcement learning and multi-agent systems
- AI ethics: responsible AI
The course uses both PyTorch and TensorFlow, so you learn the two most widely used AI frameworks. Lessons include quizzes and hands-on labs.
Who it suits
Students, IT graduates and developers who want to understand how AI works under the hood. You will need basic Python knowledge to get the most out of it.
2. ML for Beginners (12 Weeks, 26 Lessons)
ML for Beginners (github.com/microsoft/ML-For-Beginners) focuses on classic machine learning rather than deep learning. It is one of the most popular learning repositories on GitHub, with around 90,000 stars.
- Regression, classification and clustering
- Natural language processing and time series forecasting
- An introduction to reinforcement learning
- Building a simple web app that uses a trained model
- 52 short quizzes (before and after each lesson)
It uses Python and Scikit-learn, with some lessons also available in R. It is a good starting point if you are new to data science, and a natural first step before AI for Beginners.
3. Generative AI for Beginners (21 Lessons)
Generative AI for Beginners (github.com/microsoft/generative-ai-for-beginners) teaches you to build applications with large language models, the technology behind tools like chatbots and AI assistants.
- Prompt engineering (basic and advanced)
- Building chat, text-generation and image-generation apps
- Search apps with embeddings and vector databases, and retrieval-augmented generation (RAG)
- Function calling, AI agents, fine-tuning and small language models
- Responsible and secure AI
Code samples are in Python and TypeScript. Some lessons need access to an AI model through a service such as Azure OpenAI or the OpenAI API, but the course also explains how to run models locally without a cloud subscription.
Do You Get a Certificate?
No. The three GitHub courses are free learning materials, not certified programmes, and they do not issue certificates. Be careful of anyone offering a paid “Microsoft certificate” for completing them.
If you want something to show employers, combine the courses with these free Microsoft options:
- Microsoft Learn modules and learning paths: free on learn.microsoft.com. Completing them earns badges and trophies on your public Microsoft Learn profile.
- Microsoft Applied Skills: free, hands-on lab assessments that give you a verifiable credential when you pass. Available topics change over time. In 2026 they include AI-related skills such as using GitHub Copilot for AI-assisted development, so check the current list on Microsoft Learn.
- Your own projects: the labs in these courses are real code. Put your finished projects on GitHub and link them on your CV and LinkedIn profile. For many employers, a working project says more than a certificate.
How to Start AI for Beginners in 10 Minutes
- Create a free GitHub account at github.com if you do not have one.
- Open the course at github.com/microsoft/AI-For-Beginners and read the course setup lesson.
- Choose how to run the code:
- In your browser, using GitHub Codespaces or the Binder link (no installation needed), or
- On your computer, by cloning the repository and opening it in Visual Studio Code with Python installed.
- Follow the lessons in order, about two lessons a week, and do the quizzes and labs.
- Save your work to your own GitHub account by forking the repository, so you build a portfolio as you learn.
Tip for data costs: The repository includes many translations. If you download it to your computer, the course instructions explain how to download only the English lessons to save data.
Which Course Should You Start With?
- Complete beginner with some Python: start with ML for Beginners, then move on to AI for Beginners.
- Want to understand neural networks and deep learning: go straight to AI for Beginners.
- Want to build chatbots and AI apps quickly: start with Generative AI for Beginners.
- Need a credential for job applications: add Microsoft Learn modules and a free Applied Skills assessment.
How These Courses Can Help Your Career
AI skills are increasingly listed in job adverts for IT, data, finance and even administrative roles. Completing a structured course and building a few projects can strengthen applications for internships, graduate programmes and learnerships. List the course under “Training” on your CV, and link to your GitHub projects.
Frequently Asked Questions
Is Microsoft’s AI for Beginners course really free?
Yes. It is open-source course material on GitHub under the MIT licence. You only pay for your own internet data.
Does AI for Beginners give a certificate?
No. For a credential, use free Microsoft Learn modules (badges) and Microsoft Applied Skills assessments.
Do I need to know programming?
Basic Python helps a lot. If you are completely new to coding, learn Python basics first, then start with ML for Beginners.
Can I do it on my phone?
You can read the lessons on a phone, but you will need a laptop or computer to run the code properly.
How long does it take?
AI for Beginners is designed as a 12-week course with 24 lessons, about two lessons per week.
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Conclusion
Microsoft’s free AI courses are some of the best ways to learn artificial intelligence without paying a cent. Start with the course that matches your level, build real projects as you go, and add a free Microsoft Learn credential if you need something to show employers.
Wiz Admissions is an independent website and is not affiliated with Microsoft. Course details are based on Microsoft’s official GitHub repositories and Microsoft Learn, and may change.