Ollama & Local LLMs: Fine-Tune, Deploy, Build Python AI Apps

About this course

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.

In this course, you will

Wire up Ollama from Python using chat, streaming, and generation patterns that repeat across the whole curriculum.

Ship Streamlit front ends on top of local models, including

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