In general terms, we will consider two approaches to data analysis and processing - supervised learning (with a teacher) and unsupervised learning (without a teacher). The main difference is that the former uses tagged data to aid in forecasting, while the latter does not. But both approaches have more subtle differences and key areas in which they excel.
What is Supervised Learning?
Supervised Learning is a machine learning approach based on the use of datasets of labeled data. Such datasets are used to create algorithms aimed at classifying data or accurately predicting results. Using labeled inputs and outputs, the model can match inputs and outputs for accuracy and gradually train.
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: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning