Git, GitHub, and VS Code: A Beginner‑Friendly Guide for AI Developers

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.

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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.py

• final_model_v2.py

• final_model_real_final.py

Version control solves this problem by keeping a clear history of every change.

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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).

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3. Basic Git Concepts (Very Important)

Repository (Repo)

A repository is a project folder that Git tracks.

Example:

my-ai-project/

├── train.py

├── model.py

├── requirements.txt

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 (or master) → stable code

• experiment-bert

• data-cleaning

Merge

Merging means combining changes from one branch into another.

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4. Common Git Commands (Beginner Level)

Here are the most used Git commands:

• git init → Create a new Git repository

• git status → See current changes

• git add . → Stage all changes

• git commit -m "message" → Save a snapshot

• git log → View commit history

You do not need to memorize everything on day one.

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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

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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.

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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.

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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.txt

• environment.yml

• README.md

• .gitignore

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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.

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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:

data/

*.ckpt

.env

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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

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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.

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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:

git --version

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Step 2: Sign in to GitHub from VS Code

1. Open VS Code

2. Click the Accounts icon (bottom-left corner)

3. Choose Sign in with GitHub

4. Authorize VS Code in your browser

Once signed in, VS Code can communicate directly with GitHub.

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Step 3: Create or Open a Project Folder

• Open VS Code

• Click File → Open Folder

• Select your project (for example, an AI project)

Example:

ai-text-classifier/

├── train.py

├── inference.py

├── requirements.txt

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Step 4: Initialize Git Repository in VS Code

1. Click the Source Control icon (branch icon on the left)

2. Click Initialize Repository

VS Code now starts tracking your files using Git.

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Step 5: Make Your First Commit (Visually)

1. Modify or add files

2. Go to Source Control panel

3. You will see changed files listed

4. Enter a commit message (example: Initial AI project setup)

5. Click Commit

No terminal commands needed.

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Step 6: Publish the Repository to GitHub

1. In Source Control, click Publish Branch

2. Choose Public or Private repository

3. VS Code creates a GitHub repository automatically

Your code is now live on GitHub.

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Step 7: Push Changes to GitHub

Whenever you:

• Edit code

• Add features

• Fix bugs

Just:

1. Commit changes in VS Code

2. Click Sync Changes or Push

Your updates appear on GitHub instantly.

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Step 8: Pull Changes from GitHub

If changes are made on GitHub (or by teammates):

1. Open Source Control

2. Click Pull or Sync Changes

VS Code updates your local files safely.

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Step 9: Working with Branches in VS Code

Branches are heavily used in AI experiments.

1. Click the branch name in the bottom-left

2. Choose Create New Branch

3. Name it (example: experiment-lora)

4. Work freely without affecting main

Merge later when satisfied.

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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.

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Step 11: Typical AI Workflow Using VS Code + GitHub

1. Create repo in VS Code

2. Push to GitHub

3. Create experiment branch

4. Train / tune model

5. Commit results

6. Merge best experiment

This is how real AI teams work.

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VS Code removes much of Git’s fear and makes GitHub usable even for non-experts.

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13. Essential VS Code Extensions for AI Developers

Recommended extensions:

• Python

• Jupyter

• GitLens

• Pylance

• Docker (optional)

These improve productivity and code quality.

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14. Typical AI Development Workflow

1. Create a Git repository

2. Write code in VS Code

3. Commit changes regularly

4. Push to GitHub

5. Experiment using branches

6. Document with README

This workflow is used in real‑world AI teams.

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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

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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.

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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.