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G328 Supervised And Unsupervised Machine Learning

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Supervised and Unsupervised Machine Learning
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DQ 1: Difference between Supervised and Unsupervised Machine Learning
Supervised machine learning is gaining knowledge about a subcategory of machines and
artificial intelligence that uses labeled database to train algorithms that helps classifying data
outcomes accurately. In Supervised learning, the machine is trained using well-labeled data. One
can compare it to the learning process, which takes place in a supervisor or a teacher's presence.
The model is fed with data, adjusting its weight until the model fits appropriately. Unsupervised
machine learning is machine training that does not need supervision. Instead, the model works
for itself in discovering information and deals with unlabeled data. It can be too unpredictable,
and it allows one to perform complex processes. Supervised learning allows one to collect data
and produce output from previous experiences, helping one optimize performance and solve
various real-world problems. On the other hand, unsupervised machine learning helps find all
unknown patterns in data helping in finding features useful in categorizing data. Unsupervised
learning takes place in real-time, making it easier for learners to get unlabeled data from a
computer as it does not need manual intervention compared to labeled data.
The health care sector is one of the early adopters of technological advances of machine
learning, playing a key role in many health-related settings, including handling patient data,
developing new medical procedures, and treating chronic diseases. Many healthcare uses
machine learning to help pathologists make quick and accurate diagnoses of diseases and
identify the beneficiaries of the new treatment. Another example is using machine learning to
share patient’s medical data privately (Mazlan, Binti Sahabudin, Ramli, Ismail, Mohamad, et al.,
2021). A model-trained machine can be used to distribute patient data around the world by
inputting patients’ scans, which will be transferred to a centralized server registered as a
consensus model and, therefore, get clinically used.

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1 Supervised and Unsupervised Machine Learning Student’s Name Institution Affiliation Professor’s Name Course Date 2 DQ 1: Difference between Supervised and Unsupervised Machine Learning Supervised machine learning is gaining knowledge about a subcategory of machines and artificial intelligence that uses labeled database to train algorithms that helps classifying data outcomes accurately. In Supervised learning, the machine is trained using well-labeled data. One can compare it to the learning process, which takes place in a supervisor or a teacher's presence. The model is fed with data, adjusting its weight until the model fits appropriately. Unsupervised machine learning is machine training that does not need supervision. Instead, the model works for itself in discovering information and deals with unlabeled data. It can be too unpredictable, and it allows one to perform complex processes. Supervised learning allows one to collect data and produce output from previous experiences, helping one optimize performance and solve various real-world problems. On the other hand, unsupervised machine learning helps find all unknown patterns in data helping in finding features usef ...
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