Machine Learning

Machine Learning (ML) is a branch of Artificial Intelligence (AI) that enables computers to learn from data and improve their performance automatically without being explicitly programmed. It focuses on developing algorithms and models that analyze large amounts of data, recognize patterns, and make predictions or decisions with minimal human intervention. Machine Learning helps organizations uncover insights, forecast outcomes, and automate complex processes, making it a crucial technology in today’s data-driven world.

ML systems continuously improve as they are exposed to more data, which allows them to deliver increasingly accurate results over time. This technology is used across various domains—such as finance, healthcare, e-commerce, manufacturing, and education—for applications like fraud detection, recommendation systems, predictive maintenance, image and speech recognition, and autonomous systems.

To build intelligent systems capable of improving automatically through experience.

Supervised Learning

Learns from labeled data (e.g., classification, regression).

Unsupervised Learning

Finds patterns in unlabeled data (e.g., clustering, association).

Semi-Supervised Learning

Uses both labeled and unlabeled data for better accuracy.

Reinforcement Learning

Learns by interacting with the environment through rewards and penalties.