Introduction to Machine Learning

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

1. Collect data — gather examples of the problem you’re solving.
2. Prepare data — clean, organise, and transform data into a usable form.
3. Train a model — fit an algorithm to your training data.
4. Evaluate performance — test how well the model performs on new data.
5. 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:

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