🚀 6 Free Generative AI Courses to Build Real Skills

Stop just prompting. Start building.

Here are 6 free courses that will take you from zero to building production-ready Generative AI applications 👇

1️⃣ Microsoft Generative AI for Beginners

21 lessons covering everything you need to start building

🔗 Start on GitHub

What you’ll learn:

• Fundamentals of Generative AI
• How Large Language Models work
• Prompt engineering best practices
• Building AI-powered applications
• Ethical considerations and responsible AI

Best for: Complete beginners wanting a comprehensive foundation

2️⃣ Function Calling and Data Extraction with LLMs

Learn how to use LLM function calling to connect with tools and extract structured data

🔗 Start on DeepLearning.AI

What you’ll learn:

• Function calling fundamentals
• Connecting LLMs to external tools and APIs
• Extracting structured data from unstructured text
• Building reliable tool-use patterns

Best for: Developers who want their LLMs to interact with the real world

3️⃣ Building Your Own Database Agent

Learn how to interact with databases using natural language

🔗 Start on DeepLearning.AI

What you’ll learn:

• Building agents that query databases
• Natural language to SQL translation
• Safe database interaction patterns
• Handling complex queries

Level: Beginner-friendly (optional Python/SQL knowledge helpful)

Best for: Anyone who wants to build natural language interfaces to data

4️⃣ LangChain for LLM Application Development

A beginner-friendly course on building LLM-powered apps with LangChain

🔗 Start on DeepLearning.AI

What you’ll learn:

• LangChain fundamentals and architecture
• Chains, agents, and memory
• Building multi-step workflows
• Integrating external data sources

Level: Beginner-friendly (basic Python knowledge helpful)

Best for: Developers ready to build production LLM applications

5️⃣ Building and Evaluating Advanced RAG Applications

Learn the latest RAG (Retrieval-Augmented Generation) techniques

🔗 Start on DeepLearning.AI

What you’ll learn:

• Advanced RAG architectures
• Retrieval optimization techniques
• Evaluating RAG system performance
• Handling edge cases and failures

Level: Intermediate (basic Python knowledge recommended)

Best for: Developers building knowledge-grounded AI systems

6️⃣ Building Agentic RAG with LlamaIndex

Learn how to quickly build agents that can reason over your documents

🔗 Start on DeepLearning.AI

What you’ll learn:

• LlamaIndex fundamentals
• Building agentic RAG systems
• Multi-step reasoning over documents
• Query engines and routing

Level: Intermediate (basic Python knowledge recommended)

Best for: Developers building document reasoning systems

📋 Suggested Learning Path

Here’s a recommended order to tackle these courses:

Minimum viable: Courses 1 + 2 + 3 (~2-3 weeks) for solid fundamentals

💡 Key Insights

1. These Are Short, Focused Courses

Unlike semester-long university courses, these are micro-courses designed to be completed in hours, not weeks:

• Most are 1-3 hours
• Hands-on coding from day one
• No fluff, just building

2. Don’t Hoard Courses — Complete Them

It’s tempting to bookmark all these courses and feel productive. But:

Watching 10% of 10 courses < Completing 1 course 100%

Pick one course. Finish it. Then pick the next.

3. Build Alongside Learning

Don’t just watch lectures. For each course:

• Complete the coding exercises
• Build a mini-project using the concepts
• Share what you build (GitHub, blog, social)

🎓 What Makes These Courses Special

You’re learning from the teams building the tools — not just talking about them.

The knowledge to build real AI applications is now free. The question is: what will you build with it?

📚 Additional Resources

• DeepLearning.AI — More short courses
• LlamaIndex Docs — Official documentation
• LangChain Docs — Official documentation
• Hugging Face — Models and datasets

This post is part of the Informatics series on AI education and career development.