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FreeAcademy

Rutas de aprendizaje

AI Engineer

Go from Python basics to building real AI systems: understand how LLMs work, master prompting, then build RAG pipelines and AI agents step by step.

7 cursos~12h en total100% gratis, con certificado en cada curso

Insignia de la ruta

Completa todos los cursos de esta ruta para obtener su insignia.

Empieza con el paso 1

Cursos de esta ruta

  1. Paso 1Principiante~4h

    Python for Beginners

    Learn Python from scratch by building 8 mini-projects you run in your browser, from a tip calculator to a flashcard quiz app. No install, no signup to start, and a free certificate. Powered by Pyodide.

  2. Paso 2Principiante40 min

    How LLMs Actually Work: Tokens, Parameters, and MoE Explained

    Go one level under the hood of ChatGPT, Claude, and Gemini. Understand tokens, next-token prediction, context windows, model parameters, Mixture of Experts, and why AI models cost money to run. A short, no-code course for people who use AI every day.

  3. Paso 3Principiante~5h

    Prompt Engineering Course: Master AI Prompts with Hands-On Practice

    Master the art of crafting effective AI prompts through hands-on exercises. Learn prompt structure, few-shot learning, chain-of-thought reasoning, and advanced techniques with instant feedback. Build real-world prompts for code generation, content writing, and data analysis.

  4. Paso 4Intermedio

    Vector Databases: The Foundation of AI Apps

    Master vector databases for AI applications. Learn embeddings, similarity search, and hands-on setup of Pinecone, pgvector, and Chroma. Understand indexing strategies, hybrid search, performance optimization, and how to choose the right database for your use case.

  5. Paso 5Principiante75 min

    Local RAG for Beginners: Build a Private Knowledge Base

    Build a private, offline knowledge base over your own notes and PDFs using a local AI model. A hands-on beginner micro course: run a model with Ollama, turn documents into embeddings, store them in a local Chroma database, and ask questions answered from your own files. No cloud APIs, no fees, your data stays on your machine. Want to deploy it as a web app instead? See the Full-Stack RAG course (/courses/fullstack-rag-nextjs-supabase-gemini).

  6. Paso 6Principiante35 min

    Build Your First AI Agent in 30 Minutes

    Build a working AI agent from scratch in Python. Learn the think-act-observe loop, call an LLM, give your agent a tool it can use, and handle a failed tool call. Framework-agnostic and beginner-friendly.

  7. Paso 7Intermedio

    Agentic AI with Python — LangChain & LangGraph

    Build autonomous AI agents with Python using LangChain and LangGraph. Learn tool calling, stateful workflows, RAG-powered agents, multi-agent systems, and production deployment. From the ReACT pattern to a full customer support agent capstone.

Proyectos prácticos

Demuestra lo que aprendiste construyendo algo real. Elige un proyecto, constrúyelo en tu equipo, súbelo a un repositorio público de GitHub y envía el enlace para revisión.

Proyecto 1Intermedio4-6 horas

AI Research Assistant

Build a tool that accepts a research question, searches external sources, and uses an LLM to produce a structured answer with citations.

Requisitos

  • Accepts a user question from the command line or a simple UI
  • Searches at least one external source (web search API, Wikipedia, or arXiv)
  • Uses an LLM API to synthesize the findings
  • Cites the sources it used in the final answer
  • Produces structured output (sections or JSON, not one blob of text)
  • Handles errors: bad input, failed requests, empty search results

Herramientas sugeridas: Python or JavaScript, An LLM API, A search API

Proyecto 2Intermedio5-8 horas

Chat With Your Documents

Build a small RAG pipeline: load your own documents, embed them into a vector store, and answer questions grounded in what the documents actually say.

Requisitos

  • Loads a folder of text, Markdown, or PDF documents
  • Chunks the documents and stores embeddings in a vector store
  • Retrieves relevant chunks for each question
  • Answers using only the retrieved content and says which document it came from
  • Says "I don't know" when the documents don't cover the question
  • README explains how to run it with your own documents

Herramientas sugeridas: Python, An embeddings model, A vector store such as Chroma or FAISS

Proyecto 3Avanzado6-10 horas

AI Agent That Completes a Task

Build an agent that plans and executes a multi-step task using tools, for example researching a topic and saving a report, or triaging files in a folder.

Requisitos

  • The agent has at least two tools it can call (search, read file, write file, call an API)
  • Runs a reasoning loop: decide, act, observe, repeat until done
  • Logs each step so a reviewer can follow what it did
  • Has a stop condition and a maximum step limit
  • README explains the task, the tools, and one example run

Herramientas sugeridas: Python, LangChain or a hand-written agent loop, An LLM API