what is the difference between supervised learning and unsupervised learning?

If you are looking for a way to create deeper business and customer insights, in either a marketing or IT department, it is important to understand the difference between supervised and unsupervised learning. The two approaches are used for solving different problems, so there is no use in comparing them or using them interchangeably.

Supervised learning uses what we know about data sets to define how weights and biases should be adjusted. It does this by telling the algorithm whether it got a question correct or not.

Unsupervised learning instead tells the algorithm nothing about the data sets which it's trying to analyze, other than that they contain patterns of some sort.

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It's critical to grasp the distinction between supervised and unsupervised learning if you're searching for a technique to gain deeper business and consumer insights in either a marketing or IT department. Because the two methodologies are meant to solve distinct issues, comparing them or employing them interchangeably is pointless.

What we know about data sets is used to describe how weights and biases should be modified in supervised learning. It accomplishes this by informing the algorithm whether or not it answered a question correctly.

Unsupervised learning, on the other hand, informs the algorithm nothing about the data sets it's trying to evaluate except that they have patterns.

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