Supervised learning and unsupervised learning are commonly used terms in machine learning but many are unclear of their definitions and differences. To help you understand the differences between supervised learning and unsupervised learning, we have provided explanations of the two machine learning tasks:
Supervised learning is a process of learning algorithm from previously trained database. When supervised learning is performed, you’d enter input variables and specify an output variable and use an algorithm to understand the process from input to output. An example when you setup a campaign objective (e.g. new Facebook page likes) in Facebook advertising, Facebook uses supervised learning to identify an audience that’s most aligned with your objective and serve ads to this audience.
The goal of supervised learning is to predict output variables based on new input data.
Unsupervised learning does not have defined outputs or variables. You’re simply entering input data and using a machine to unravel patterns or structure about the data.
For example, you can use unsupervised learning by entering customer information and the machine will define patterns or clusters of data based on customers’ age groups, geographic locations, or purchase frequency.
If you have any questions about the supervised learning, unsupervised learning, or machine learning, please feel free to get in touch below!
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