5 Free Microsoft GitHub Courses for Data Science and AI

You do not need expensive courses to learn data science and artificial intelligence. Microsoft has created several complete, free learning curricula on GitHub with lessons, quizzes, assignments, code examples, and projects.

AI learning has also expanded far beyond traditional data science and machine learning. Today, you can learn about large language models (LLMs), retrieval-augmented generation (RAG), fine-tuning, generative AI, tool use, and AI agents alongside the fundamentals.

Below are five free Microsoft GitHub courses that can take you from data science and machine learning basics to generative AI and agentic systems.

1. Data Science for Beginners

Microsoft’s Data Science for Beginners is a great starting point if you are completely new to data science.

The 10-week curriculum includes 20 lessons covering data ethics, statistics and probability, relational and non-relational data, data preparation, visualization, and analysis. You also get quizzes, assignments, and practical exercises using real datasets.

The course introduces the complete data science workflow instead of focusing too much on individual tools, giving you a solid foundation before moving on to machine learning.

2. Machine Learning for Beginners

Once you are comfortable working with data, Microsoft’s Machine Learning for Beginners is the logical next step.

It is a 12-week curriculum with 26 lessons covering regression, classification, clustering, natural language processing, time-series forecasting, and reinforcement learning. You will use Python and Scikit-learn to train and evaluate machine learning models.

This course focuses on classical machine learning rather than jumping straight into LLMs. Understanding concepts such as training, evaluation, features, and model performance makes it much easier to learn more advanced AI later.

3. Artificial Intelligence for Beginners

Microsoft’s Artificial Intelligence for Beginners expands on machine learning and introduces the broader foundations of artificial intelligence.

The 12-week curriculum contains 24 lessons covering neural networks, computer vision, natural language processing, deep learning, symbolic AI, and reinforcement learning. Topics include PyTorch, TensorFlow, convolutional neural networks, recurrent neural networks, embeddings, transformers, transfer learning, autoencoders, and generative adversarial networks.

This course is well suited for those who want to understand what is happening inside AI models rather than only learning how to use them through an API.

4. Generative AI for Beginners

For those interested in modern AI applications, Microsoft’s Generative AI for Beginners is arguably the most immediately relevant course on this list.

The curriculum includes 21 lessons covering prompt engineering, model selection, responsible AI, function calling, retrieval-augmented generation (RAG), vector databases, AI agents, fine-tuning, security, small language models, and open-source models. The repository also includes practical examples using Python and TypeScript.

This course goes beyond prompting and introduces many of the components that make up the modern generative AI stack. The learning materials are free, though some exercises may require access to an AI API or Microsoft Foundry. The course also supports running some models locally using Foundry Local.

5. AI Agents for Beginners

After learning the basics of generative AI, Microsoft’s AI Agents for Beginners covers the next frontier.

The course contains 18 lessons focused on building agentic AI systems, including tool use, agentic RAG, planning, memory, context engineering, multi-agent systems, MCP, computer-use agents, security, and deploying agents into production. Practical examples use technologies such as the Microsoft Agent Framework and Microsoft Foundry Agent Service.

This is the most advanced course on the list, reflecting where AI development is increasingly headed — systems where models can retrieve information, use tools, maintain context, and complete multi-step tasks. Completing at least some of the Generative AI for Beginners course first is recommended, as familiarity with LLMs, RAG, and tool calling makes the agent material much easier to follow.

Final Thoughts

Learning modern AI and machine learning is no longer mainly about money, tools, or access. Today, you can find free courses, free APIs, open-source models, free development platforms, and even free GPU access.

The barrier to getting started is much lower than it used to be. Understanding the fundamentals of AI is increasingly valuable regardless of your profession — whether you are a doctor, developer, researcher, or business professional, knowing how AI systems make decisions, how they are optimized, and where they can improve existing workflows is becoming an important skill across nearly every industry.