How Machine Learning Works

Machine Learning (ML) is a subset of Artificial Intelligence that enables computers to learn from data and improve performance over time without being explicitly programmed. ML powers recommendation systems, predictive analytics, fraud detection, and many other applications.

Core Concepts of Machine Learning

Data: Raw information used to train and evaluate models.
Features: Individual measurable properties or attributes of data used for prediction.
Labels: Target outcomes used in supervised learning.
Algorithms: Procedures that learn patterns from data, e.g., decision trees, linear regression, neural networks.
Model: The result of training an algorithm on data, capable of making predictions or decisions.

Types of Machine Learning

Supervised Learning: The model learns from labeled data (inputs and corresponding outputs).
Unsupervised Learning: The model identifies patterns in unlabeled data, such as clustering or association.
Reinforcement Learning: The model learns by interacting with an environment, receiving rewards or penalties for actions.