If you are starting your journey in AI, Machine Learning, or software development, you will quickly hear terms like Git, GitHub, repository, commit, and VS Code. At first, these can feel confusing. This article explains everything step by step in simple English, with a focus on how AI developers actually use these tools.
1. Why Version Control Matters (Especially in AI)
When building AI projects, you often:
Experiment with different models
Change datasets and preprocessing steps
Tune hyperparameters
Collaborate with others
Without version control, files get messy:
final_model.pyfinal_model_v2.pyfinal_model_real_final.py
Version control solves this problem by keeping a clear history of every change.
2. What Is Git?
Git is a distributed version control system.
In simple terms:
Git tracks changes in your code over time and lets you go back to any previous version.
Key Features of Git
Works locally on your computer
Keeps a full history of changes
Allows safe experimentation
Makes collaboration easier
Git was created by Linus Torvalds (the creator of Linux).
3. Basic Git Concepts (Very Important)
Repository (Repo)
A repository is a project folder that Git tracks.
Example:
Commit
A commit is a snapshot of your project at a specific time.
Think of it as:
“I want to save my work exactly as it is now.”
Each commit has:
A unique ID
Author name
Date and time
Commit message
Branch
A branch lets you work on new features without breaking the main code.
Common branches:
main(ormaster) → stable codeexperiment-bertdata-cleaning
Merge
Merging means combining changes from one branch into another.
4. Common Git Commands (Beginner Level)
Here are the most used Git commands:
git init→ Create a new Git repositorygit status→ See current changesgit add .→ Stage all changesgit commit -m "message"→ Save a snapshotgit log→ View commit history
You do not need to memorize everything on day one.
5. What Is GitHub?
GitHub is a cloud platform that hosts Git repositories.
In simple words:
Git is the tool, GitHub is the place where your code lives online.
Why GitHub Is Important for AI Developers
Share projects publicly
Collaborate with teams
Contribute to open‑source AI libraries
Showcase your work to employers
Access thousands of AI repositories
6. Git vs GitHub (Clear Difference)
| Git | GitHub |
|---|---|
| Version control tool | Online hosting platform |
| Works offline | Requires internet |
| Tracks file changes | Stores and shares repos |
You can use Git without GitHub, but GitHub uses Git internally.
7. Important GitHub Concepts
Remote Repository
A remote repo is the online version of your project on GitHub.
Clone
Cloning downloads a GitHub repository to your local computer.
Push
Push uploads your local commits to GitHub.
Pull
Pull downloads the latest changes from GitHub.
Fork
A fork is your personal copy of someone else’s repository.
This is common in open‑source AI projects.
8. GitHub for AI & Machine Learning Projects
Typical AI repositories include:
Training scripts
Model architectures
Configuration files
Dataset instructions
Research papers
Common AI‑related files:
requirements.txtenvironment.ymlREADME.md.gitignore
9. What Is README.md and Why It Matters
README.md explains your project.
A good README includes:
Project purpose
Installation steps
How to run the code
Dataset details
Model explanation
For AI developers, README quality matters a lot.
10. What Is .gitignore?
.gitignore tells Git which files NOT to track.
In AI projects, you should ignore:
Large datasets
Model checkpoints
API keys
Temporary files
Example:
11. What Is VS Code?
Visual Studio Code (VS Code) is a code editor developed by Microsoft.
It is extremely popular among AI developers.
Why VS Code Is Loved by AI Developers
Free and lightweight
Excellent Python support
Built‑in Git integration
AI‑friendly extensions
12. Using Git and GitHub Inside VS Code (Practical Guide)
This section explains step by step how beginners can practically use GitHub from VS Code, without memorizing many commands.
Step 1: Install Prerequisites
Make sure you have:
Git installed on your system
VS Code installed
A GitHub account
After installing Git, verify it by running in terminal:
Step 2: Sign in to GitHub from VS Code
Open VS Code
Click the Accounts icon (bottom-left corner)
Choose Sign in with GitHub
Authorize VS Code in your browser
Once signed in, VS Code can communicate directly with GitHub.
Step 3: Create or Open a Project Folder
Open VS Code
Click File → Open Folder
Select your project (for example, an AI project)
Example:
Step 4: Initialize Git Repository in VS Code
Click the Source Control icon (branch icon on the left)
Click Initialize Repository
VS Code now starts tracking your files using Git.
Step 5: Make Your First Commit (Visually)
Modify or add files
Go to Source Control panel
You will see changed files listed
Enter a commit message (example:
Initial AI project setup)Click Commit
No terminal commands needed.
Step 6: Publish the Repository to GitHub
In Source Control, click Publish Branch
Choose Public or Private repository
VS Code creates a GitHub repository automatically
Your code is now live on GitHub.
Step 7: Push Changes to GitHub
Whenever you:
Edit code
Add features
Fix bugs
Just:
Commit changes in VS Code
Click Sync Changes or Push
Your updates appear on GitHub instantly.
Step 8: Pull Changes from GitHub
If changes are made on GitHub (or by teammates):
Open Source Control
Click Pull or Sync Changes
VS Code updates your local files safely.
Step 9: Working with Branches in VS Code
Branches are heavily used in AI experiments.
Click the branch name in the bottom-left
Choose Create New Branch
Name it (example:
experiment-lora)Work freely without affecting
main
Merge later when satisfied.
Step 10: Resolving Merge Conflicts (Beginner View)
If two changes clash:
VS Code highlights conflicts clearly
You choose which code to keep
Save and commit
VS Code makes conflict resolution beginner-friendly.
Step 11: Typical AI Workflow Using VS Code + GitHub
Create repo in VS Code
Push to GitHub
Create experiment branch
Train / tune model
Commit results
Merge best experiment
This is how real AI teams work.
VS Code removes much of Git’s fear and makes GitHub usable even for non-experts.
13. Essential VS Code Extensions for AI Developers
Recommended extensions:
Python
Jupyter
GitLens
Pylance
Docker (optional)
These improve productivity and code quality.
14. Typical AI Development Workflow
Create a Git repository
Write code in VS Code
Commit changes regularly
Push to GitHub
Experiment using branches
Document with README
This workflow is used in real‑world AI teams.
15. GitHub and Open‑Source AI
Most popular AI libraries live on GitHub:
PyTorch
TensorFlow
Hugging Face Transformers
LangChain
Learning GitHub helps you:
Read real production code
Report issues
Contribute fixes
Learn best practices
16. Common Beginner Mistakes
Not committing often
Uploading large datasets
Writing poor commit messages
Ignoring README
Fear of Git commands
Mistakes are normal. Git is designed to protect you.
17. Final Thoughts
Git, GitHub, and VS Code are essential tools, not optional skills, for AI developers.
Start small:
Track one project
Push it to GitHub
Improve step by step
With time, these tools will feel natural and powerful.
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