Machine Learning (ML)

Machine Learning (ML) refers to a subset of Artificial Intelligence (AI) that enables computers to learn from data and improve their performance over time without being explicitly programmed. It is an automated process that empowers machines to recognize and identify patterns in large amounts of data, extract meaningful insights, and make predictions or decisions based on this information. ML algorithms are designed to analyze and interpret data, learn from past experiences, and continually optimize their performance by adapting to new data inputs.

The fundamental concept behind ML is that instead of providing explicit instructions, machines use statistical techniques to learn from data patterns and make accurate predictions or decisions. Various types of ML algorithms exist, such as supervised learning, unsupervised learning, and reinforcement learning, each tailored for specific tasks and datasets. ML finds numerous applications in different fields, including image and speech recognition, natural language processing, recommendation systems, fraud detection, healthcare diagnostics, and more. The rapid growth of ML has been driven by advancements in computational power, big data availability, and algorithmic improvements, making it a powerful tool for solving complex problems and enhancing automation in various domains.