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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. In this article, we will explore 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. I like that the course introduces the complete data science workflow instead of focusing too much on individual tools. It gives you a solid foundation before you move on to machine learning. 2. Machine Learning for Beginners Once you are comfortable working with data, Microsoft's Machine Learning for Beginners is the next course I would recommend. It is a 12-week curriculum with 26 lessons covering regression, classification, clustering, natural language processing, time-series forecasting, and reinforcement learning. You will also use Python and Scikit-learn to train and evaluate machine learning models. What I like about this course is that it focuses on classical machine learning instead of immediately jumping 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. You will also explore technologies and techniques such as PyTorch, TensorFlow, convolutional neural networks, recurrent neural networks, embeddings, transformers, transfer learning, autoencoders, and generative adversarial networks. I would recommend this course if you want to understand what is happening behind AI models instead of only learning how to use them through an API. 4. Generative AI for Beginners If you are mainly interested in modern AI applications, Microsoft's Generative AI for Beginners is probably the most relevant course on this list. The curriculum currently 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 is the course I would spend the most time on if your goal is to build real LLM applications. It goes beyond prompting and introduces many of the components that now make up the modern generative AI stack. The learning materials are free, but depending on the exercises you choose, you may need 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, you can move on to Microsoft's AI Agents for Beginners. The course currently contains 18 lessons focused on building agentic AI systems. It covers tool use, agentic RAG, planning, memory, context engineering, multi-agent systems, MCP, computer-use agents, security, and deploying agents into production. The course also includes practical examples using technologies such as Microsoft Agent Framework and Microsoft Foundry Agent Service. This is probably the most advanced and current course on the list. AI development is increasingly moving toward systems where models can retrieve information, use tools, maintain context, and complete multi-step tasks. I recommend completing at least some of the Generative AI for Beginners course first. Once you understand LLMs, RAG, and tool calling, the agent-focused material becomes 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 access to GPUs. That means the barrier to getting started is much lower than it used to be. At the very least, I think everyone should understand the fundamentals of AI, regardless of their profession. Whether you are a doctor, developer, researcher, or business professional, it is becoming increasingly important to understand how AI systems make decisions, how they are optimized, and where they can improve your existing workflow. AI is becoming part of almost every industry. You do not necessarily need to become an AI engineer, but understanding how these systems work will increasingly be a valuable skill.