Structured theory, roadmaps, and practice for modern skills.
Learn AI Agents, Automation, and Code with clear roadmaps and solid theory.
Explained.Courses helps you learn AI Agents, automation tools, and programming the right way: with
well-structured roadmaps, foundational theory articles, curated video lessons, and paper-based practice
for every concept.
How Explained.Courses Works
Roadmaps, theory, and practice designed to work together.
Every topic is built in layers. Start with foundations, follow a clear monthly roadmap, and use theory
articles and practice exercises as support whenever a concept feels new or unclear.
Understand the concepts first.
Use the Theory section when you want a clean explanation of a concept: loops, JSON, APIs, LLMs,
embeddings, webhooks, or anything else. Each article is written for AI and automation learners.
Follow a monthly learning path.
Roadmaps are broken into months and weeks. Each step gives you curated YouTube lessons, homework on
paper, and tasks that slowly move you from beginner to confident practitioner.
Practice on paper and in real tools.
Below each lesson, you get small “write on paper” exercises and real tooling tasks. This helps you
remember better, think clearly, and apply concepts in code, automations, or agent workflows.
Core Learning Tracks
Choose a path that matches how you want to work.
You can learn everything in a straight line, or you can pick a specialization. Each path has its own
roadmap, theory support, and project ideas. You can always switch paths later.
Featured Roadmaps
You can start now with any of these core AI-focused learning paths. Each one begins with Month 1 –
Foundations.
AI Agent Engineer Roadmap
Learn LLMs, embeddings, tools, planning, and agent workflows step by step.
No-Code AI Automation Roadmap
Combine Zapier, Make, and n8n with LLMs to build automations clients pay for.
AI Developer Roadmap
Use Python, JavaScript, APIs, and vector stores to build AI-powered applications.
Getting Started
Start simple, then go deeper when you feel ready.
You do not have to understand everything on day one. Pick a path, follow Month 1 calmly, and use theory
articles whenever something feels unfamiliar.
Read a few theory articles.
Begin with the foundations: variables, loops, JSON, APIs, and LLM basics. This will make every roadmap
feel easier to follow.
Then /theory/automation/
Choose your first path.
Decide whether you want to focus on AI Agents, no-code automations, or AI development. Your choice is not
permanent—you can explore others later.
No-Code AI path
Follow Month 1 and actually practice.
Watch the curated videos, write answers on paper below each lesson, and try the suggested exercises in
real tools. Progress comes from practice, not speed.
Weekly practice tasks