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Introduction to Commonly used Algorithms
he algorithms that transform a data set into a model are kno n as machine learning algorithms, and they are the heart
of machine learning. he appropriate algorithm to use supervised, unsupervised, classification, regression, etc.) ill rely
on the type of problem you're trying to solve, the computational po er you have at your disposal, and the type of data
you have. et us no study some commonly used AI algorithms.
Supervised Learning Algorithms
Regression Classi cation Decision Trees
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egression predicts a real number he classification algorithm Decision trees, one of the most
either rounded or ith a decimal point) categorises the dataset into popular supervised learning
as the last component of the result. groups based on several criteria. algorithms, are named for their
he technique ill use both dependent he AI model learns from the tree like structure even if the tree is
variable and an independent variable to training dataset hen employing a inverted). he roots of the tree are
predict a potential result. classification method, and it then the training datasets, hich connect
divides the data into several groups to particular nodes that represent test
based on hat it has discovered. attribute nodes. A node that doesn't
continue on is referred to as a leaf
since nodes frequently lead to other
nodes. y branching out into sub
nodes until it reaches the conclusion,
the decision tree can classify the data
that is presented to it.
K Nearest Neighbour Algorithm
It is one of the simplest upervised earning based achine earning algorithms. he Y
algorithm assumes similarity bet een the ne case and the e isting cases and
assigns the ne instance to the category that matches the e isting cases the most
closely. he algorithm can be applied to both classification and regression.
Other examples include - Support-vector machines, Naive Bayes, and Linear Category B
discriminant analysis, and K Nearest. New Data Point
Category A
X
C apstone P roj e ct 135

