Supervised Learning - Artificial Cognition and Machine Technology Today

Supervised Learning is a fundamental approach in machine learning where models are trained on labeled datasets, meaning that each training example is paired with an output label. The goal is to learn a mapping from inputs to outputs so that the model can make accurate predictions on new, unseen data. This process involves two main phases: training and testing. During training, the algorithm learns by adjusting its internal parameters to minimize the difference between its predictions and the actual labels, often using techniques such as gradient descent and loss functions. Supervised learning is typically categorized into two types: classification, where the output labels are discrete categories (e.g., spam detection or image classification), and regression, where the output is a continuous value (e.g., predicting house prices or stock prices). Popular algorithms used in supervised learning include linear regression, decision trees, support vector machines, and neural networks. Its wide range of applications spans various domains, including finance (credit scoring), healthcare (disease diagnosis), and marketing (customer segmentation), making supervised learning a cornerstone of many practical machine learning solutions.