Model Anatomy · Episode 00 · 30-second cut · 0:38
What is machine learning? Watch a line learn house prices from 40 examples
No rules are written. It guesses a price, measures the error and nudges two numbers 2,000 times, ending at price = 3.33 × size + 2.44, and then prices a house it has never seen.
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/00-what-is-machine-learning python3 src/learn.py
Project structure00-what-is-machine-learning/
Project structure
machine-learning/00-what-is-machine-learning/Open on GitHub →
- data/
- houses.csv40 houses: size and price
- scripts/
- make_houses.pymade houses.csv (you don't need to run it)
- src/
- houses.pyloads houses.csv into x and y
- learn.pymainthe 8 lines from the video
- README.mdthe idea, how to run it, a line-by-line walkthrough, things to try
machine-learning/00-what-is-machine-learning/src/learn.pyOpen on GitHub →
import numpy as np
from houses import x, y # 40 examples
w, b = 0.0, 0.0 # the model
for epoch in range(2000):
err = w * x + b - y # how wrong
w -= 0.01 * (err * x).mean() # nudge w
b -= 0.01 * err.mean() # nudge b
print(f"price = {w:.2f} * size + {b:.2f}")Transcript
Nobody told this line where to go. It figured that out by itself. That's machine learning. You don't write rules.
You show it examples. Like these forty houses. Bigger house, bigger price. The model starts as a flat, clueless line.
Just two numbers. It guesses a price, checks how wrong it was, and nudges both numbers a tiny bit. Do that two thousand times, and now it can price a house it's never seen. No rules.
Just examples. That's machine learning.
Read the lesson on GitHub →- #machinelearning
- #python
- #datascience
- #ai
