Introduction to Machine Learning

Machine Learning Fundamentals with Python

2 min read

Published Nov 16 2025

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What is Machine Learning?

Machine Learning (ML) is a way of teaching computers to find patterns in data and make predictions or decisions without being explicitly programmed for every task.
Instead of hard-coding rules, we give the computer examples (data) and let it learn from them.

Examples of ML applications:

Predicting house prices based on past sales
Classifying emails as spam or not spam
Recommending movies or products
Recognising objects in images
Translating text from one language to another

At its core, ML involves training a model using data, and then using that model to make predictions on new, unseen data.

The Basic ML Workflow

Collect data — gather examples of the problem you’re solving.
Prepare data — clean, organise, and transform data into a usable form.
Train a model — fit an algorithm to your training data.
Evaluate performance — test how well the model performs on new data.
Deploy and improve — use the model in real applications and retrain as needed.

Example: Teaching a Model to Predict House Prices

Suppose we have data about houses, each example includes:

size_in_sqft
number_of_bedrooms
distance_to_city_center
price

Our goal: predict price based on the other features.

Simple Python example:

import numpy as np

from sklearn.linear_model import LinearRegression

# Example data (in real life this comes from a dataset)

# size, bedrooms, distance_from_city_center

X = np.array([

[1000, 2, 5],

[1500, 3, 3],

[800, 2, 7],

[1200, 3, 4],

[2000, 4, 2]

])

# Prices (in thousands)

y = np.array([200, 300, 180, 250, 400])

# Create and train a simple linear regression model

model = LinearRegression()

model.fit(X, y)

# Predict price for a new house

new_house = np.array([[1300, 3, 3]])

predicted_price = model.predict(new_house)

print(f"Predicted price for new house: £{predicted_price[0]*1000:.2f}")

Explanation:

We created some small example data.
We used Scikit-learn’s LinearRegression model.
We trained the model (fit) and then predicted for a new example (predict).

This simple exercise captures the essence of ML — learn from data, then generalise to new cases.

Key Terms to Remember

Feature - An input variable (e.g., house size, number of rooms)
Label / Target - The value we want to predict (e.g., house price)
Training Data - Data used to teach the model
Testing Data - Data used to evaluate how well the model generalises
Model - The mathematical representation of what the computer has learned