Day 1 — AI Setup & Git | AI Engineering Internship

0–5 min
Real Hook

5–25 min
Core Concept

25–45 min
Live Coding

45–55 min
You Code It

55–60 min
Cheat Sheet

🔥 Real-World Hook (0–5 min)

A data scientist at a Bengaluru startup trained a model for 6 hours. Accidentally deleted the script. No Git. Six hours gone.

AI work is messy — you try 10 things, keep 2. Git lets you experiment fearlessly. And venv means "it works on my machine" stops being an excuse when your model goes to production.

⚙ What You Install Today (5–25 min)

💡 The 5 Tools

Python 3.12 — language + ecosystem for all AI/ML.
VS Code — editor with Jupyter notebook support built-in.
venv — isolated Python per project (no version conflicts).
pip — install numpy, pandas, scikit-learn, torch in one command.
Git — version every experiment so you can always go back.

💻 Live Demo — AI Project Scaffold (25–45 min)

># Step 1: Create AI project
mkdir my-ai-project
cd my-ai-project

# Step 2: Virtual environment
python -m venv venv
venv\Scriptsctivate          # Windows

# Step 3: Install core AI stack
pip install numpy pandas matplotlib scikit-learn

# Step 4: Freeze dependencies
pip freeze > requirements.txt  # like package.json for Python

# Step 5: Git init
git init
git add .
git commit -m "initial AI project setup"

🧠 Your First NumPy Program

># explore.py — verify your stack works
import numpy as np
import pandas as pd

# NumPy: fast array math (the heart of all AI)
scores = np.array([85, 92, 78, 95, 88])
print(f"Mean: {scores.mean():.1f}, Std: {scores.std():.1f}" )

# Pandas: tabular data (CSV, databases, DataFrames)
df = pd.DataFrame({
    "name":  ["Priya", "Ravi", "Ananya"],
    "score": [85, 92, 78]
})
print(df)

📋 You Code It (45–55 min)

✍ Task

Create stats.py: make a list of 5 exam scores, print mean, max, min using NumPy. Then make a DataFrame with name + score columns and print it.

⚡ Day 1 Cheat Sheet

python -m venv venv

Create isolated Python env

venv\Scriptsctivate

Activate on Windows

pip install numpy pandas

Install AI stack

pip freeze > requirements.txt

Save dependencies

np.array([1,2,3])

Create NumPy array

arr.mean() / arr.std()

Stats on array

git init && git add .

Start versioning

git commit -m "msg"

Save a snapshot

Day 02 · NumPy & Arrays →