What is machine learning?

Machine learning is a branch of artificial intelligence in which computers learn patterns from data and use those patterns to make predictions, classifications, recommendations, or decisions without being explicitly programmed with every rule.

Instead of telling the computer exactly how to solve a problem step by step, we provide data and a learning algorithm. The algorithm adjusts a model so that it can map inputs to useful outputs.

Simple example

Suppose we want to predict whether an email is spam.

A machine learning system can be trained using many example emails along with information about whether each email is spam or not. From those examples, the model learns patterns such as:

• words or phrases commonly found in spam
• unusual sender behavior
• suspicious links or formatting

After training, the model can use the learned patterns to classify a new email it has never seen before.

The basic idea

A typical machine learning process looks like this:

Data → Learning Algorithm → Trained Model → Prediction on New Data

The important part is that the useful behavior is learned from data rather than being fully specified through hand-written rules.

Machine learning includes several learning approaches, such as supervised learning, unsupervised learning, and reinforcement learning. Each approach learns from data in a different way and is covered separately.

In short, machine learning allows computers to learn useful patterns from data and apply what they learned to new examples.