How Does Machine Learning Work? A Simple Explanation

20, Sep 2023

Machine learning is the technology behind most of the AI you use every day: spam filters, product recommendations, voice assistants, photo apps and chatbots. Yet "machine learning" can sound mysterious. At its core, it is a way of teaching computers to find patterns in examples instead of following hand-written rules. This guide explains how machine learning works in simple terms, step by step, with everyday examples and no maths required.

Rules versus learning

Traditional software follows instructions a programmer writes: "if the email contains these words, mark it as spam". That works until spammers change their words. Machine learning takes a different approach. Instead of writing every rule, you give the computer many examples of spam and genuine emails, and it learns the patterns that tell them apart. When new emails arrive, it applies what it learned.

Machine learning is one part of the wider field of artificial intelligence. If you are new to AI itself, start with What Is Artificial Intelligence? A Plain-English Guide.

How machine learning works, step by step

Step 1: Collect data

Everything starts with examples. To predict house prices, you need records of past sales with features such as size, location and number of rooms. To recognise cats in photos, you need many photos. The quality and variety of data matter enormously: a model can only learn patterns present in its data.

Step 2: Prepare the data

Real data is messy. It has missing values, errors and inconsistent formats. Preparing data, by cleaning it, removing duplicates and converting it into numbers a computer can process, often takes more time than any other step.

Step 3: Choose a model

A model is a mathematical structure that can be adjusted to fit data. Simple models draw straight-line relationships; decision trees split data with a series of yes or no questions; neural networks combine many simple units in layers to capture complex patterns.

Step 4: Train the model

Training means showing the model examples and adjusting its internal settings, called parameters, to reduce its mistakes. The model makes a prediction, compares it with the correct answer, measures the error and nudges its parameters to do slightly better. Repeated many times over the data, this process gradually improves the model.

Step 5: Test the model

To know whether the model has truly learned, you test it on data it has never seen. If it performs well on training data but badly on new data, it has memorised rather than learned. This problem is called overfitting.

Step 6: Use and monitor it

Once it is good enough, the model is put into use, for example inside an app. The world changes, so models need monitoring and retraining over time. Our guide How to Deploy a Machine Learning Model to Production explains this stage.

The three main types of machine learning

Supervised learning

The model learns from labelled examples, where each example comes with the correct answer: emails marked spam or not, photos labelled "cat" or "dog", houses with known prices. Most practical business uses are supervised.

Unsupervised learning

The model receives data without labels and looks for structure, such as grouping customers with similar buying habits. It is useful for exploring data and finding segments.

Reinforcement learning

The model learns by trial and error, receiving rewards for good actions. It has been used to train systems that play games and control robots, and a related technique, learning from human feedback, is used to make chatbots more helpful.

Neural networks and deep learning

Neural networks are loosely inspired by the brain. They are made of layers of connected units; each unit performs a simple calculation and passes the result on. "Deep learning" means networks with many layers, which can learn complex patterns directly from raw data such as images, audio and text. IBM's overview of neural networks explains the structure in more detail.

Deep learning powers image recognition, speech recognition, translation and the large language models behind modern chatbots.

How chatbots and generative AI use machine learning

Large language models are trained on huge amounts of text to predict the next word in a sequence. By learning those patterns at enormous scale, they become able to answer questions, summarise and write. They are then refined with further training to follow instructions and avoid harmful outputs. Image generators learn the relationship between pictures and descriptions, and speech tools learn the relationship between sounds and words.

You can see these technologies at work in TextPixAI: AI Chat uses a language model to hold conversations, the AI Image Generator turns text into images, and Speech to Text converts audio into written words.

Why machine learning makes mistakes

Bad or limited data: if the training data is incomplete or unrepresentative, the model will be too.
Bias: models can learn and repeat unfair patterns in historical data. See Fairness in Machine Learning: A Beginner's Guide.
Overfitting: memorising training examples instead of general patterns.
Change over time: a model trained on last year's data may not fit this year's world.
No real understanding: models find statistical patterns; they do not understand meaning the way people do. This is why chatbots can produce confident but false answers.

Everyday examples

Your bank flags an unusual card payment.
A streaming service suggests a film you might like.
Your phone groups photos by the people in them.
Maps predict traffic and suggest a faster route.
Email moves spam out of your inbox.

For a structured free course, Google's Machine Learning Crash Course is an excellent next step, and IBM's guide to machine learning covers the main techniques. To see how the field developed, read History of Artificial Intelligence: A Clear Timeline.

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Frequently Asked Questions

How does machine learning work in simple terms?

A computer is given many examples, finds patterns in them during training, and then uses those patterns to make predictions or decisions about new data.

What is the difference between AI and machine learning?

AI is the broad goal of making computers perform tasks that need intelligence. Machine learning is the main method used today to achieve it, by learning from data.

What is training data?

The examples a model learns from, such as labelled emails, photos or past sales. Its quality strongly affects how well the model performs.

Is deep learning the same as machine learning?

Deep learning is a type of machine learning that uses neural networks with many layers. It is especially good with images, audio and text.

Do I need to know maths to understand machine learning?

Not to understand the basic ideas. Building models professionally does require statistics, linear algebra and programming.

How Does Machine Learning Work? A Simple Explanation