Machine Learning course, Lesson 3

Supervised Learning

Learn how labelled examples help models predict numerical values and categories. Explore features, targets, regression, classification, data preparation and evaluation through eight concepts with interactive visuals, activities and quiz questions, then complete a customer-renewal business challenge.

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Concept 1 of 80 of 8 concepts completed

What Is Supervised Learning?

Explain how a model learns from examples that include known outcomes.

Understand the concept

Supervised learning trains an algorithm using examples that pair input features with a known target. The model learns a relationship between the inputs and the target, then uses that learned relationship to estimate outcomes for new cases. It does not simply memorise a guaranteed answer for every future example.

Business connection

A lender could study past applications with recorded repayment outcomes to estimate risk for new applications. Such estimates require validation, fairness checks and appropriate human oversight.

Visual explanation

From labelled examples to an estimate

Explore how features, known labels and a trained model fit together. In supervised learning, the target is known for training examples.

Input features

Features are the inputs the model receives. They must be available at the moment the prediction is made and be plausibly related to the outcome.

Interactive activity

Identify the labelled example

A subscription business wants to predict renewal. Which pair of information makes its historical records useful labelled examples for supervised learning?

Select your responseOnly customer names, with no outcome recordedA prediction made by a model, without checking the real outcomeA spreadsheet colour and the file nameCustomer inputs recorded before renewal and the actual renewal outcome

Machine Learning Basics for Business

This machine learning for beginners lesson explains the core ideas behind modern predictive tools. Instead of memorising definitions, you practise the same reasoning an analyst uses when a manager asks whether data can help with a decision.

What is machine learning?

Machine learning is a way of building software that learns patterns from examples. A machine learning model is the learned result of training, and it is useful only when its predictions are tested and used with care.

How does a machine learning model work?

A model takes input features, such as advertising spend or days since a customer last ordered, and produces an estimate of a target, such as sales or churn. Training data supplies the examples, and evaluation on unseen data shows whether the pattern holds for new cases.

Key terms

Supervised learning: Learning from examples paired with known target outcomes.
Feature: An input variable supplied to a model.
Target / label: The known outcome a supervised model is trained to estimate.
Regression: A supervised task that estimates a numerical value.
Classification: A supervised task that predicts a category or class.

Frequently asked questions

What is supervised learning?

Supervised learning uses examples with known target outcomes to learn a relationship between input features and the outcome.

What is the difference between regression and classification?

Regression estimates a numerical value, while classification predicts a category or class.

What are features and a target?

Features are input variables used by a model. The target is the outcome the supervised model is trained to estimate.

Why does labelled data matter?

Known target outcomes let the training algorithm compare its estimates with the expected outcomes and learn a relationship.

Why test a model on unseen data?

A model can fit its training examples well but perform poorly on new cases. Separate unseen data provides more honest evidence of generalisation.