**Description:**
Supervised vs. Unsupervised Learning is a foundational concept in the field of machine learning. Supervised learning involves training algorithms on labeled datasets, where the model learns to map inputs to known outputs. This method is commonly used for classification and regression tasks, enabling reliable predictions based on historical data. On the other hand, unsupervised learning works with unlabeled data, allowing the model to identify patterns, group similar data points, or detect anomalies without explicit instructions. This approach is often utilized for clustering, association, and dimensionality reduction. Understanding the differences and applications of supervised and unsupervised learning is critical for selecting the appropriate techniques for various machine learning projects.
