Get Started: Run Your First ML Code

Every tutorial in this series has live, runnable code. Before you hit that code, you need a place to run it. This takes about 5 minutes.

Pick one of the two options below.

Google Colab is a free coding environment that runs entirely in your browser. No installation, no setup, works on any computer or phone.

  1. Open your browser and go to colab.research.google.com
  2. Sign in with a Google account (Gmail works)
  3. Click New notebook in the top left
  4. You will see a blank cell, like an empty text box
  5. Click inside the cell and type this:
print("Hello, ML!")
  1. Press Shift + Enter to run it

You should see Hello, ML! appear right below the cell. That is it. You now have a working Python environment.

To run the code from any tutorial, just copy it, paste it into a new cell, and press Shift + Enter.

Option B: Python on Your Own Computer

If you prefer to work offline, use Anaconda. It installs Python and everything you need in one go.

Step 1: Download Anaconda

Go to anaconda.com/download and download the version for your system (Windows, Mac, or Linux). Run the installer and click through the defaults.

Step 2: Check it worked

Open Anaconda Prompt (Windows) or your Terminal (Mac or Linux). Type:

python --version

You should see something like Python 3.11.5. If you do, Python is installed.

Step 3: Open Jupyter Notebook

Jupyter Notebook is your coding workspace, similar to Google Colab but on your own machine. Start it by typing:

jupyter notebook

A browser window opens showing your files. Click New, then Python 3 (ipykernel). A blank notebook opens. Same experience as Colab.

Install the Libraries (Local Python Only)

If you are using Google Colab, skip this. Colab has everything pre-installed.

If you chose Option B, open your terminal and run:

pip install numpy pandas scikit-learn matplotlib

This downloads four libraries that every tutorial uses. It takes about a minute.

What Each Library Does

You will see these imports throughout every tutorial. Here is what they actually do:

Library Plain-English description
numpy Does math on large lists of numbers, very fast
pandas Works with tables of data, like Excel but in code
scikit-learn Ready-made ML models you can train in two lines of code
matplotlib Draws charts and graphs from your data

Your First ML Program

Let us make sure everything works. Copy and paste this into a cell and run it:

# Import the tools we need
import numpy as np
from sklearn.linear_model import LinearRegression

# Our training data: house size (sq ft) and price ($k)
# We are showing the model two examples so it can learn the pattern
sizes = np.array([[1000], [2000]])
prices = np.array([200, 400])

# Train the model on our two examples
model = LinearRegression()
model.fit(sizes, prices)

# Now ask the model: what would a 1500 sq ft house cost?
prediction = model.predict([[1500]])[0]
print(f"Predicted price for a 1500 sq ft house: ${prediction:.0f}k")

Expected output:

Predicted price for a 1500 sq ft house: $300k

What just happened?

You trained a machine learning model. It looked at two examples (1000 sq ft = $200k and 2000 sq ft = $400k), figured out the pattern (every extra 100 sq ft adds $20k), and used that pattern to predict that a 1500 sq ft house costs $300k. That is the entire idea of machine learning, compressed into 10 lines.

You did not write any rule telling it “add $20k per 100 sq ft.” It worked that out by itself. That is the magic.

Troubleshooting

“ModuleNotFoundError: No module named sklearn” You need to install the libraries. Run pip install scikit-learn in your terminal.

“I don’t have a Google account” Create a free one at accounts.google.com or use the local Python option.

“The cell is not running” Make sure you clicked inside the cell first, then pressed Shift + Enter (both keys at the same time).

You are set up. Let us start learning.

Next: What is Machine Learning?