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Semi-supervised machine learning is a type of machine learning paradigm that falls between supervised learning and unsupervised learning. In semi-supervised learning, the training dataset contains a combination of labeled and unlabeled data. This approach is particularly useful when obtaining a large amount of labeled data is costly or time-consuming, but
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Unsupervised machine learning is a category of machine learning where the algorithm is trained on unlabeled data, meaning that it's given input data without any corresponding output or target variable. Unlike supervised learning, where the algorithm learns to make predictions based on labeled data (input-output pairs), unsupervised learning
Inside AI
Supervised machine learning in Artificial Intelligence (AI) and Machine Learning (ML) is a type of machine learning where an algorithm learns from a labeled dataset, which means that each input in the dataset is paired with the corresponding desired output. The goal of supervised learning is to learn a mapping