suresh Explaining the Difference Between Supervised and Unsupervised Learning
The focus keyword for this topic is "supervised and unsupervised learning." In the realm of machine learning, there are two main approaches: supervised learning and unsupervised learning.
Supervised Learning:
Supervised learning involves training a model using labeled data. This means that the dataset used to train the model includes input-output pairs, where the output is known. The model learns to map inputs to outputs, making predictions or classifications based on the patterns it identifies in the training data. Supervised learning is commonly used in tasks such as regression and classification.
Unsupervised Learning:
Unsupervised learning, on the other hand, involves training a model on unlabeled data. In this approach, the model tries to find patterns or structure in the data without explicit guidance. Unsupervised learning techniques are used for tasks such as clustering and dimensionality reduction.
In summary, supervised learning relies on labeled data for training, while unsupervised learning works with unlabeled data to discover hidden patterns or structure. Choosing the right approach depends on the nature of the data and the desired outcome of the machine learning task.
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