Model Anatomy · Episode 02 · 30-second cut · 0:34
K-means clustering explained: finding groups in data with zero labels
Drop 3 pins into 300 dots, let every dot join its closest pin, move each pin to the middle of its crowd and repeat. You end with 3 clean groups of 100: unsupervised learning at its simplest.
What you’ll needPython 3.10+, NumPy
What you’ll need
- python.org/downloads →
Python 3.10 or newerRuns the code. Check yours with python3 --version. - code.visualstudio.com →
VS Code or CursorEither works; so does any editor you like. - git-scm.com/downloads →
gitGets the code from GitHub. 
NumPy 1.26+Math on many numbers at oncepip install "numpy>=1.26"
Set it up
- 1Get the code
git clone https://github.com/DayanEbrar0X/data-anatomy.ai.git cd data-anatomy.ai
- 2Make a virtual environmentIt keeps this project's packages separate from the rest of your computer.
python3 -m venv .venv source .venv/bin/activate
- 3Install the packagesrequirements.txt installs every lesson's packages. This one needs only: NumPy.
pip install -r requirements.txt # or just this lesson: pip install "numpy>=1.26"
- 4Run it
cd machine-learning/02-k-means python3 src/kmeans.py
Project structure02-k-means/
Project structure
machine-learning/02-k-means/Open on GitHub →
- data/
- shops.csv300 shop locations (x, y), no labels
- scripts/
- make_shops.pymade shops.csv
- src/
- kmeans.pymainthe 8 lines from the video
- shops.pyloads shops.csv into X
- README.mdthe idea, how to run it, a line-by-line walkthrough, things to try
machine-learning/02-k-means/src/kmeans.pyOpen on GitHub →
import numpy as np
from shops import X # 300 unlabeled shops
C = X[[234, 263, 286]] # 3 random guesses
for step in range(8):
d = ((X[:, None] - C)**2).sum(2)
g = d.argmin(1) # nearest center
C = [X[g == j].mean(0) for j in (0, 1, 2)]
print("group sizes:", *np.bincount(g))Transcript
Three hundred dots. Zero labels. Can a computer find the groups on its own? Yep.
It's called k-means, and it's weirdly simple. Drop three pins anywhere. Every dot runs to its closest pin. Then each pin slides to the middle of its crowd.
Repeat. Dots switch teams, pins slide again, until nothing moves. Three clean groups. A hundred each.
Nobody labeled a thing. That's k-means.
Read the lesson on GitHub →- #kmeans
- #clustering
- #machinelearning
- #datascience
