Machine learning is a branch of artificial intelligence where systems learn patterns from data instead of following hand-written rules. It sits behind fraud detection, demand forecasting, product recommendations and the large language models that power today's AI assistants.
This guide explains five common algorithms in plain English, with an example of how each is used in business.
A machine learning model is trained on historical examples. In supervised learning, the examples include the answer, such as past sales or whether a transaction was fraudulent, and the model learns to predict it. In unsupervised learning, there's no answer, and the model finds structure on its own, such as customer segments. The trained model is then tested on data it hasn't seen before using it for real decisions.
How it works: fits a straight-line relationship between inputs and a numeric outcome, such as how price and advertising affect sales.
Business use: forecasting sales or costs, and understanding which factors drive them. Its strength is that results are easy to explain.
How it works: splits data into branches using a series of yes/no questions, ending in a prediction.
Business use: credit decisions, customer churn and triage, where people need to see the reasoning behind a prediction.
How they work: combine many decision trees to produce more accurate, stable predictions than a single tree.
Business use: some of the most widely used methods for structured business data, such as predicting equipment failures, customer lifetime value or demand.
How it works: finds the boundary that best separates two or more classes of data.
Business use: classification problems with clear categories, such as sorting documents or detecting defects in images, especially with smaller data sets.
How it works: layers of connected nodes learn complex patterns, especially in images, sound and text.
Business use: computer vision for safety and quality inspections, speech recognition and the large language models behind generative AI.
The most complex algorithm isn't always the best. Start with the business question, the data you have and how much explanation people need. Often a simpler, explainable model that's well integrated into a process delivers more value than a sophisticated one nobody trusts. Today, many problems can also be solved with pre-built AI services rather than custom models.
Want to apply machine learning in your organisation? Our AI and machine learning consultants can help you choose the right approach and get it into production.
