What You’ll Learn: Git for AI Workflows
You’ll go from “I broke my repo” to confidently managing AI projects with full version history, branching, and collaboration — all in two weeks.
Git Fundamentals
Commits, staging, branching, merging, and undoing mistakes — the core of reproducible work.
GitHub Basics
Repositories, pull requests, issues, and READMEs — how teams share AI code.
Large File Handling
Use Git LFS to version-control datasets, model weights, and notebooks without bloating your repo.
Reproducible Sharing
Structure your project so others can run it exactly as you did — no more “it works on my machine.”
Who Is This Course For?
Perfect for anyone starting AI projects and tired of messy folders, lost code, or broken collaboration.
- Students building portfolios or capstone projects
- Researchers sharing code with peers
- Developers starting open-source AI contributions
Hands-On Projects
Personal AI Portfolio
Organize all your notebooks and scripts into a clean, versioned GitHub repo with a professional README.
Model Experiment Tracker
Use branching to test different model architectures and compare results cleanly.
Capstone
Collaborative Mini-Project
Team up (or simulate it) to build a shared AI project using issues, PRs, and protected branches.
2-Week GitHub Syllabus
~20 hours total • Lifetime LMS access • 1:1 mentor support
Week 1: Git & Local Workflows
- Installing Git & GitHub CLI
- Configuring user identity
- Staging, commits, and history
- Branching and merging
- Undoing changes (reset, revert, checkout)
- Ignoring files with .gitignore
Week 2: GitHub & Collaboration
- Creating and cloning repos
- Pushing, pulling, and syncing
- Working with GitHub Desktop (GUI)
- Managing large files with Git LFS
- Writing READMEs and documentation
- Using issues and pull requests
- Best practices for AI project structure
NSTC‑Accredited Certificate
Share your verified credential on LinkedIn, resumes, and portfolios.
Frequently Asked Questions
We’ll use both GUI (GitHub Desktop) and CLI so you can choose your comfort level. By the end, you’ll be confident in the terminal too.
Yes! You’ll learn Git LFS (Large File Storage) to track datasets, notebooks, and model weights without breaking your repo.
