Supervised learning is a machine learning approach that’s defined by its use of labeled datasets. This post will clarify the differences so you can choose the best approach for your situation. However, there are some nuances between the two approaches, and key areas in which one outperforms the other. The main difference is one uses labeled data to help predict outcomes, while the other does not. Within artificial intelligence (AI) and machine learning, there are two basic approaches: supervised learning and unsupervised learning. You can see them in use in end-user devices (through face recognition for unlocking smartphones) or for detecting credit card fraud (like triggering alerts for unusual purchases). The world is getting “smarter” every day, and to keep up with consumer expectations, companies are increasingly using machine learning algorithms to make things easier. Find out which approach is right for your situation. In this article, we’ll explore the basics of two data science approaches: supervised and unsupervised.
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