Trilhas de aprendizado
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.
Selo da trilha
Conclua todos os cursos desta trilha para ganhar o selo.
Cursos desta trilha
- Etapa 1Iniciante~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.
- Etapa 2Iniciante40 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.
- Etapa 3Iniciante~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.
- Etapa 4Intermediário
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.
- Etapa 5Iniciante75 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).
- Etapa 6Iniciante35 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.
- Etapa 7Intermediário
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.
Projetos práticos
Prove o que aprendeu construindo algo real. Escolha um projeto, desenvolva na sua máquina, publique em um repositório público do GitHub e envie o link para revisão.
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
Ferramentas sugeridas: Python or JavaScript, An LLM API, A search API
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
Ferramentas sugeridas: Python, An embeddings model, A vector store such as Chroma or FAISS
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
Ferramentas sugeridas: Python, LangChain or a hand-written agent loop, An LLM API