Supervised learning and unsupervised learning are two fundamental approaches in machine learning, each serving distinct purposes:

1. Supervised Learning:
   • Definition: Supervised learning involves training a model on labeled data, where each input data point is paired with a corresponding target output. The goal is for the model to learn a mapping from inputs to outputs, based on examples provided during the training phase.
   • Objective: The primary objective of supervised learning is to make predictions or classify new data points based on the patterns learned from the labeled training data.
   • Examples: Common supervised learning tasks include image classification (identifying objects in images), regression (predicting continuous values like house prices), and natural language processing (language translation, sentiment analysis).
2. Unsupervised Learning:
   • Definition: Unsupervised learning involves training a model on unlabeled data, where the algorithm tries to learn the underlying structure or patterns within the data without explicit guidance or predefined outcomes.
   • Objective: The main objective of unsupervised learning is to find hidden patterns, groupings, or relationships within the data.
   • Examples: Clustering is a typical unsupervised learning task, where the goal is to group similar data points together (e.g., customer segmentation based on purchasing behavior). Dimensionality reduction techniques, such as principal component analysis (PCA), are also unsupervised methods used to simplify and summarize data.