Next session: 20 Nov 2026
AI Courses
Start building your AI skills with our AI courses
Artificial Intelligence is rapidly transforming how software is built and how organizations operate. From AI-assisted development and copilots to intelligent applications, professionals who know how to apply AI effectively gain a clear advantage. Standing still equals falling behind.
The real question is: how will you use AI in your work? Whether you want to work more efficiently with AI tools, build copilots and agents, integrate AI into applications or learn how to use data to train models, our AI training helps you develop the practical skills to start applying AI today.
Working Smarter with AI |
1
day |
1
day |
Copilot Studio |
Agentic Coding |
3
days |
3
days |
AI Engineering for Developers |
1
day |
Data Science with Python |
Python Development |
4
days |
Microsoft 365 |
2
days |
Work Smarter with AI tools
Many professionals want to benefit from AI today. In our courses Working Smarter with AI: ChatGPT and Working Smarter with AI: Microsoft 365 Copilot, you learn how to use modern generative AI tools to enhance your daily workflow, automate tasks, analyze information and create simple, practical AI solutions.
Ready for the next step? In our follow-up course Working Smarter with AI: Skills, Second Brain and Agents, you learn more advanced techniques to give AI agents a deep understanding of you, your organization and your business processes. Using context engineering, reusable skills and connections to your own tools and data, you build agents that take over complete workflows and automate your processes from start to finish.
Working Smarter with AI Learning Path
LOW-CODE AI DEVELOPMENT
Standard AI tools like ChatGPT are powerful out of the box, but remain relatively generic and limited when it comes to integrating with your own data, processes and systems.
Low-code platforms like Microsoft Copilot Studio address this by enabling organizations to build tailored, AI-driven agents that connect to internal data sources, automate workflows and support real business scenarios. They are especially valuable when customization and integration are required, without the need for technical or programming expertise.
Our course Building your own agents with Microsoft Copilot Studio teaches you how to quickly create and deploy custom agents tailored to your business needs.
Agentic Coding
Many developers are exploring how they can use AI Coding in their daily work. The technology is evolving very quickly. The guidelines and best practices are being rewritten almost every month. That makes it exciting, but also difficult for development teams to know what really works in practice.
Teams that manage to use these tools effectively already have a clear advantage. They are not just using AI tools. They are learning how to work differently.
Do you want to get started with Agentic Coding? Or are you already using these tools, but not yet getting the results you expected?
That is why we offer training courses on GitHub Copilot and Claude Code, helping development teams understand how the role of the developer is changing in the AI era.
Vibe Coding, Done Right: Build Your Own App
Have an idea for an app but no programming background? With vibe coding, you build a real web or mobile application together with an AI coding agent.
In the 3-day course Practical Vibe Coding for Non-Developers, you learn to turn your idea into clear requirements and to steer, review and correct the agent's work. You also learn just enough about architecture, databases, security and deployment to stay in control.
Developing AI-Powered Apps
Modern AI platforms such as Azure AI, combined with evolving libraries and agent frameworks, make it possible to build and deploy LLM-based solutions and agents in a structured and scalable way. This becomes especially relevant when customization, control and integration with existing applications are key. With the right approach, developers can add advanced AI capabilities to their applications, without needing to be data scientists.
To support this, we offer two dedicated courses: one focused on C# developers and one tailored for Python developers.
AI Development with C#
Our course Developing AI-Powered Apps with C# and Azure AI is tailored for professional C# developers. Gain hands-on experience with key libraries such as Microsoft.Extensions.AI and Microsoft Agent Framework and learn to seamlessly integrate pre-built AI services and Large Language Models into your .NET apps.
AI Development with Python
Our course Developing AI-Powered Apps with Python and Azure AI is tailored for professional Python developers. Gain hands-on experience with key libraries such as Microsoft Agent Framework and the Microsoft Foundry SDK and learn to seamlessly integrate pre-built AI services and Large Language Models into your Python projects.
Building MCP Servers
Want Claude, ChatGPT or GitHub Copilot to work with your own systems? MCP is the open standard that connects AI applications to your tools and data.
In our one-day course Building MCP Servers, you learn to turn your own APIs, data and workflows into an MCP server. You build it once, and every MCP-compatible AI client and agent can use it. You also learn how to secure your server and deploy it to the cloud, so your organization can use AI on its own data safely.
Data Science & Machine Learning
While tools like LLMs and copilots focus on integrating AI into applications, data science is about building custom models tailored to specific business problems. When accuracy, control or domain-specific insights are critical, pre-built AI is often not sufficient.
Techniques such as neural networks and classical machine learning allow organizations to train models on their own data and solve targeted challenges like forecasting, anomaly detection or classification. To support this, we offer the following course:
Azure Machine Learning with Python
Learn how to develop, train and deploy machine learning models using Python and Azure Machine Learning. Our course Building your own Models with Azure Machine Learning focuses on practical implementation, from data preparation to model operationalization in a cloud environment.
