Build a real AI assistant on your own laptop — even if you've never written a line of Python.This is a hands-on, beginner-first course that takes you from "what is Python?" to shipping a multi-persona AI assistant that runs entirely on your machine using LM Studio. No paid API keys. No credit card. No rate limits while you learn.What makes this course different- Local-first - Everything runs on your laptop with free, open-source tools.- Build, don't watch - Every concept is taught through a Python file you actually run.- Two files per topic - A short demo file for follow-along and a longer annotated file to study at your own pace.- Real projects - Three graded assignments and a capstone — all QA-flavored so you finish with a portfolio piece.- No frameworks until you need them- Pure Python and the OpenAI SDK — that's it.By the end of this course you'll be able to- Set up a professional Python project with venv, .env and .gitignore.- Run a Large Language Model on your own laptop with LM Studio / Ollama.- Make your first Python → LLM API call.- Build multi-turn chatbots with memory and system prompts.- Use the same code with cloud providers like Open Router when you're ready.- Code 5× faster with GitHub Copilot.- Ship a menu-driven Personal AI Assistant with multiple personas.What you'll build1. Test Case Catalog — a pure-Python data project (Assignment 1).2. Bug Report Generator — your first real LLM-powered tool (Assignment 2).3. QA Agent — a multi-skill agent that plans, triages, summarises and assesses risk (Assignment 3).4. Personal Life Assistant — capstone with multiple AI personas and a menu-driven loop.5. Defect Triage Assistant with persistent cross-conversation memory.
What you'll learn
Set up a professional Python project with virtual environments and version control.
Run a large language model locally using LM Studio.
Make API calls to AI models using Python.
Develop chatbots that can hold multi-turn conversations.
Utilize GitHub Copilot to code more efficiently.
Course objectives
Complete three graded projects and a capstone assignment.
Achieve proficiency in running AI models locally.
Build practical AI tools applicable to various scenarios.