"how numbers are stored and used in computers"
Machine learning is a subfield of artificial intelligence (AI) that focuses on developing computer systems that can learn from data to make informed decisions. Unlike traditional software, where a developer explicitly programs instructions to solve a problem, machine learning models automatically identify patterns and relationships in data to make predictions or produce some useful output.
Machine learning fundamentally involves training a model on a dataset, where a model is any computer program designed to improve its performance over time without being explicitly programmed for each specific outcome. This training process involves adjusting the model's parameters based on the input data and the desired outputs, often using mathematical optimization techniques to minimize error.
There are several types of machine learning, each suited for different tasks.
In supervised learning, the model is trained on labeled data—data that includes both the input and the correct output—making it well-suited for tasks such as classification and regression. In unsupervised learning, the model is given data without labeled outputs and must find hidden patterns or groupings on its own, such as in clustering or dimensionality reduction. Reinforcement learning, another category, involves an agent that learns by interacting with an environment and receiving feedback in the form of rewards or penalties.
Machine learning has become a critical tool across industries, powering applications such as spam filters, recommendation systems, fraud detection, speech recognition, and autonomous vehicles. Its success depends not only on the sophistication of the algorithms but also on the quality and quantity of data available for training. As the field continues to evolve, machine learning is expected to play an increasingly central role in solving complex, data-driven problems.