If you are tired of black-box cloud APIs and want AI that runs on your hardware, keeps your data local, and still ships like a real product, this course gives you a complete path from first Python call to a deployable model and a capstone app you can show in a portfolio.
You start with Olloma + Python (chat, streaming, generation patterns you reuse everywhere), then move into Streamlit apps, RAG-style assistants, multi-agent workflows, Unsloth + QLoRA fine-tuning, export into Ollama, and finish with a full local coding assistant (browser, editor, diffs, run code, shell, optional web research and vision hooks). Theory never floats alone: every idea maps to working code and a clear next step.
What makes this course practical. You build, not only watch. Expect real tools: chat UIs, PDF Q&A, embedding search, personal note and diary apps, CrewAI agent teams, Whisper + Ollama video Q&A, and a large Streamlit coding IDE powered by Ollama. You also fine-tune a small instruct model, compare base vs fine-tuned answers, merge adapters, and ship the result through Ollama, including straight talk on quantization and quality so your exports behave the way you expect.
