Supervised and Unsupervised Machine Learning Algorithms in Data Science

Running head: MACHINE LEARNING
MACHINE LEARNING
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MACHINE LEARNING1
Introduction
Machine learning is used in various fields, the algorithms are very useful for various
purposes, this essay describes regarding two vital algorithms that are used by the field of data
science, and the algorithms are supervised ML algorithms and unsupervised ML algorithms
(Meng, Bradley & Yavuz, 2016). This essay described regarding this in a detailed manner. It
further presents a real world example of how artificial neural nets use supervised learning for
predicting outcomes in decision making.
Body
Two main types of machine learning algorithms are supervised ML algorithms and
Unsupervised ML algorithms. Supervised ML algorithms are the algorithms which involves
the process of direct supervision of operation (Xiao, Rasul & Vollgraf, 2017). The developers
are responsible for labelling sample data corpus along with setting boundaries that are very
strict in nature and on these the algorithm operated. Most widely utilized supervised ML
algorithms are logical regression, random forest, linear regression, neural networks and many
more (Burrell, 2016). Unsupervised ML algorithms does not involve complete direct control
of a particular developer. The difference among the two algorithms include the fact that
supervised ML algorithms use labeled data in an exclusive manner whereas unsupervised ML
algorithms learn feeds on various data that are unlabeled (Abadi, Barham & Chen, 2016).
Most widely used unsupervised ML algorithms include K-means clustering, t-SNE, PCA and
some more.
Artificial neural nets make use of supervised learning for the purpose of predicting
outcomes in decision making
Neural nets are types of deep learning models that make use of large amount of
training data in order to identify various correlations between various variables for learning to