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suresh
suresh - Supervised Learning: In supervised learning, the model is trained on a labeled dataset, where each input example is associated with a corresponding output label. The goal is to learn a mapping function that can predict the output label for new, unseen input data. This type of learning requires the presence of a target variable to guide the learning process.
- Unsupervised Learning: Unsupervised learning involves training the model on an unlabeled dataset, where the data is not structured or classified in any way. The goal is to find hidden patterns or structures in the data without the need for explicit input-output pairs. Clustering and dimensionality reduction are common tasks in unsupervised learning.
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