Supervised vs Unsupervised Learning
The terms like supervised learning and unsupervised learning are used in the context of machine learning and artificial intelligence that are gaining in importance with each passing day. Machine learning, for the layman, is algorithms that are data driven and make a machine learn with the help of examples. There are two types of learning; namely, supervised learning and unsupervised learning that confuse students as there are many similarities between the two. However, despite overlapping, there are differences that will be highlighted in this article.
In coming years, we are likely to witness an increase in the development of machine learning to make dealing with business problems easier and faster. Hiring of employees to tackle simple business problems would become obsolete using the concepts of supervised and unsupervised learning.
What is Supervised Learning?
This is a type of learning where machine learning takes place with the help of inputs from users. Much of the research in the field of machine learning and artificial intelligence till date has focused upon supervised learning. For example, the spam folder in your email gets full with sometimes even important mails going to it unintentionally. The system works on the basis of machine learning that notifies an algorithm pertaining to analysis of spam. The system uses the information to filter messages and send them to spam folder reducing false positives. In a search engine, the algorithm works on the basis of the link clicked first when it opens search results. This leads to improvements in the search results for a user. However, there are certain drawbacks in supervised learning as the machine has a vague idea of what is right and what is wrong. This human feedback often puts limitations to the future use of supervised learning.
What is Unsupervised Learning?
We are living in times where we are looking for better performance from machines all the time whether it is CCTV data, GPS data, online transaction data, machine scan data, security scan data, and so on. Organizations and governments want machines that do not need or require supervised data from humans to turn in better results. This of course requires putting in a lot more effort in the direction of automation, and though it is unlikely for unsupervised learning to replace supervised learning in the near future, the hybrid approaches are likely to emerge in the near future that will be faster and more efficient than the results that we are getting through supervised learning at present.
What is the difference between Supervised and Unsupervised Learning?
• Supervised learning and unsupervised learning are two different approaches to work for better automation or artificial intelligence.
• In supervised learning, there is human feedback for better automation whereas in unsupervised learning, the machine is expected to bring in better performances without human inputs.
• Hybrid approaches are more likely solutions in the near future that make use of both supervised and unsupervised learning.
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