Already know basic Python? Build one real thing end to end: a small AI data app that loads a dataset, sends a slice of it to a language model, and turns the response into a written analysis. You will use pandas only as much as the app needs, call an LLM API directly (no agents, no frameworks), wire the pieces into a single script with real error handling, and ship it as a shareable Streamlit app you can run locally. A project-based intermediate course with a free certificate of completion.
3 módulos • 7 aulas
Understand how data moves through modern systems, no code required. Learn ETL vs ELT, cloud storage with Amazon S3, AWS Glue basics, data validation, and pipeline monitoring, plus how AI tools help you design, explain, and document pipelines.
Level up from Python basics to writing powerful, real-world Python for data, AI, and automation. Master classes and dataclasses, comprehensions and generators, decorators, context managers, robust error handling, type hints, packaging, working with files and APIs, and a practical intro to async. Hands-on examples and a free certificate of completion.
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.
Learn Python from zero with a focus on AI and data science. Master the language essentials, then use ChatGPT, Claude, and Gemini as coding tutors while you build real projects with NumPy, pandas, matplotlib, and scikit-learn. Perfect for university students and early-career learners. No prior coding required, and you earn a free certificate to add to your LinkedIn and resume.
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.
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).