Supervised Learning
Supervised learning is a foundational ML approach in which an algorithm learns from labeled data, meaning that each training example consists of input features along with the corresponding correct output. The goal of the model is to learn the relationship between the inputs and outputs so that it can accurately predict outcomes for new, unseen data.
This method is widely used in tasks such as classification and regression. Classification involves predicting categorical labels, such as detecting whether an email is spam or not, while regression involves predicting continuous values, such as house prices or stock market trends. The model iteratively adjusts its internal parameters using optimization techniques like gradient descent to minimize errors.
Some of the most common supervised learning algorithms include linear regression, decision trees, support vector machines (SVMs), and neural networks. These algorithms are widely used in real-world applications, including fraud detection, medical diagnosis, speech recognition, and customer churn prediction.