Neural networks (machine learning) is a computational model within the field of machine learning, inspired by the structure and function of biological neural systems. It consists of an interconnected set of nodes, often referred to as artificial neurons, typically organized in layers. These nodes process input data through weighted connections and activation functions, propagating information through the network. The model learns to perform specific tasks, such as pattern recognition, classification, or regression, by adjusting the weights and biases of these connections through exposure to training data, a process commonly involving optimization algorithms. This architecture is a fundamental tool for learning complex, non-linear relationships and is central to many advanced machine learning applications.