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Machine learning is a branch of artificial intelligence that focuses on building systems capable of learning patterns from data and making decisions or predictions without being explicitly programmed for every specific task. Among the core paradigms of machine learning, supervised learning and unsupervised learning are two fundamental approaches that differ primarily in the nature of the data they use and the type of problems they solve. Understanding these two approaches is essential for grasping how modern intelligent systems function across domains such as healthcare, finance, marketing, and natural language processing. Supervised learning is a type of machine learning where the model is trained using labeled data. In this context, labeled data means that each training example consists of an input along with the correct output. The goal of supervised learning is to learn a mapping from inputs to outputs so that when new, unseen data is presented, the model can predict the corresponding output accurately. The process involves feeding a dataset into a learning algorithm, which then iteratively adjusts its internal parameters to minimize the difference between its predictions and the actual labels. This difference is often quantified using a loss function, and optimization techniques such as gradient descent are used to reduce this loss. There are two primary categories of supervised learning problems: classification and regression. In classification tasks, the output variable is categorical, meaning the model predicts discrete labels or classes. For example, determining whether an email is spam or not spam, identifying whether an image contains a cat or a dog, or diagnosing a disease based on patient symptoms are classification problems. In regression tasks, the output variable is continuous, meaning the model predicts numerical values. Examples include predicting house prices based on features like ____ __________ ___ ______ ____ __________ ______ _______ _________ _________ ______ _________.
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