Answer / Madhulika Kumari

Supervised and unsupervised learning are two main categories of machine learning algorithms. Supervised learning uses labeled training data, where both input features and desired output labels are provided for each example. The goal is to learn a function that can map inputs to outputs based on the training examples. Common supervised learning algorithms include linear regression, logistic regression, support vector machines (SVM), and neural networks. In contrast, unsupervised learning focuses on finding patterns or structures in the data without explicit labels. Clustering and dimensionality reduction are common examples of unsupervised learning techniques.

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