Getting Started with AI

Why Git?

Every AI project involves experiments — different model architectures, hyperparameters, datasets. Git lets you track every change, revert mistakes, and collaborate with others. It's non-negotiable in professional AI work.

Install Git

brew install git

sudo apt update && sudo apt install git

Configure Git

git config --global user.name "Your Name"
git config --global user.email "you@example.com"
git config --global init.defaultBranch main

Essential Commands

# Start a new project
git init my-ai-project
cd my-ai-project

# Track changes
git add .
git commit -m "Initial commit"

# Connect to GitHub
git remote add origin https://github.com/yourusername/my-ai-project.git
git push -u origin main

# Clone an existing project
git clone https://github.com/huggingface/transformers.git

Install Python via pyenv (recommended)

pyenv lets you manage multiple Python versions — essential when different AI projects need different versions.

# macOS / Linux
curl https://pyenv.run | bash

# Add to your shell (~/.zshrc or ~/.bashrc)
export PYENV_ROOT="$HOME/.pyenv"
export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"

# Install Python 3.11 (best for AI in 2025)
pyenv install 3.11.9
pyenv global 3.11.9
python --version  # Should show 3.11.9

Windows users: download Python 3.11 from python.org and check "Add to PATH".

Virtual Environments

Always use a virtual environment. It isolates your project's packages from the system Python.

# Create a virtual environment
python -m venv .venv

# Activate it
source .venv/bin/activate      # macOS/Linux
.venv\Scripts\activate         # Windows

# Your prompt will show (.venv)
# Install packages
pip install numpy pandas scikit-learn

# Save dependencies
pip freeze > requirements.txt

# Deactivate when done
deactivate

Core AI Packages

pip install numpy pandas matplotlib scikit-learn
pip install torch torchvision          # PyTorch
pip install transformers datasets      # HuggingFace
pip install jupyter ipykernel          # Notebooks

VS Code — Your Primary Editor

Download from code.visualstudio.com. Then install these essential extensions:

# Open VS Code from terminal
code .

# Select Python interpreter (Ctrl+Shift+P)
# > Python: Select Interpreter
# Choose your .venv

Jupyter Notebooks

Notebooks let you run code cell by cell — perfect for exploring data and experimenting with models.

# Start Jupyter in your project folder
jupyter notebook

# Or use JupyterLab (better UI)
pip install jupyterlab
jupyter lab

Terminal Setup

# macOS: use iTerm2 + zsh (already default on macOS)
# Windows: use Windows Terminal + WSL2 for Linux environment

# Useful aliases to add to ~/.zshrc or ~/.bashrc
alias jl="jupyter lab"
alias activate="source .venv/bin/activate"
alias gs="git status"

What you'll build

A sentiment classifier that reads a sentence and tells you if it's positive or negative. You'll use a pre-trained HuggingFace model — no training required.

Step 1 — Create your project

mkdir my-first-ai && cd my-first-ai
git init
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install transformers torch

Step 2 — Write the script

Create a file called sentiment.py:

from transformers import pipeline

# Load a pre-trained sentiment model (downloads ~250MB first time)
classifier = pipeline("sentiment-analysis")

# Test it
sentences = [
    "I love learning about AI, it's fascinating!",
    "This is really frustrating and confusing.",
    "The model performance was acceptable.",
]

for sentence in sentences:
    result = classifier(sentence)[0]
    label = result['label']
    score = result['score']
    emoji = "😊" if label == "POSITIVE" else "😞"
    print(f"{emoji} [{label} {score:.2f}] {sentence}")

Step 3 — Run it

python sentiment.py

Step 4 — Save your work with Git

echo ".venv/" > .gitignore
echo "__pycache__/" >> .gitignore
git add sentiment.py .gitignore
git commit -m "feat: first AI sentiment classifier"

What's next?