Supervised Learning: Uses labeled data to train a model to make predictions or decisions. It requires input-output pairs and aims to learn a mapping from inputs to outputs. Unsupervised Learning: Uses unlabeled data to find hidden patterns or structures. It aims to explore the data's underlying structure without predefined labels.
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Supervised Learning: Uses labeled data to train a model to make predictions or decisions. It requires input-output pairs and aims to learn a mapping from inputs to outputs. Unsupervised Learning: Uses unlabeled data to find hidden patterns or structures. It aims to explore the data's underlying structure without predefined labels.
1rahulN/supervised-and-unsupervised-learning-using-deeplearning
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Supervised Learning: Uses labeled data to train a model to make predictions or decisions. It requires input-output pairs and aims to learn a mapping from inputs to outputs. Unsupervised Learning: Uses unlabeled data to find hidden patterns or structures. It aims to explore the data's underlying structure without predefined labels.
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