A subset of AI that enables systems to learn patterns from data without explicit programming
Machine Learning represents the computational approach that enables systems to improve through experience rather than through explicit programming. At its core, ML algorithms detect patterns within data and build mathematical models that can make predictions or decisions based on new inputs. The discipline encompasses three primary learning paradigms: supervised learning (training on labeled examples), unsupervised learning (finding hidden patterns in unlabeled data), and reinforcement learning (learning optimal behaviors through trial-and-error interaction with an environment). Modern ML extends beyond traditional statistical methods through its ability to handle high-dimensional data, learn complex non-linear relationships, and improve performance as more data becomes available. What distinguishes machine learning from conventional software development is its focus on creating frameworks that enable systems to derive their own rules and representations rather than following predetermined instructions.