AI agents are everywhere. Most tutorials skip the part where you understand them. Before you can build an AI agent that actually works, you need to know what’s happening underneath. How does a language model produce text? Why does it forget what you said? Why does the same question give different answers? What is your code really sending when it “talks” to a model? This course answers those questions from the ground up, with clear animations instead of jargon. How this course works Each lecture covers one idea in about five minutes. You’ll see every concept animated first, so it clicks before any code appears, and each section ends with a quick check so you know it stuck.
What you’ll learn:
- How LLMs really work: next-word prediction, training, and why models sometimes invent facts
- Tokens: what models actually read, and how that affects cost, limits and speed
- The context window: why models have no memory, and how chat apps work around it
- Temperature: how models choose words, and which setting to use when
- APIs and JSON: the plumbing that lets your Python code talk to a model
- API keys: how to keep them safe, and what to do if one leaks
- Your first LLM call: messages, roles and multi-turn conversations in real Python code
- What makes an agent: the model, tools and loop behind every AI agent
Who it’s for
This course is for Python developers, software engineers and curious learners who want a solid, honest foundation in modern AI. You only need basic Python. No machine learning or maths background is required. By the end, you’ll understand the building blocks every AI agent is made from, and you’ll be ready to start building your own.
