Methodology of the classification of the problem will differ

Methodology

In a world where machines capable of
thinking on their own are a dream of many to create, Machine learning has made
it achievable to make smarter and intelligent decisions in the IoT, which turns
out to be one of the reasons for the success of the Internet of Things.

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Figure 2: Learning from the available
data without being explicitly programmed

Source: Gartner
(January 2017)

Machine learning Algorithms are
essentially characterized into three major categories. They are as follows:

·        
Supervised Machine Learning Algorithms.

·        
Unsupervised Machine Learning
Algorithms.

·        
Reinforcement Machine Learning
Algorithms.

 

Supervised Machine learning algorithms
consists of an input and as well as an output variable which are used for predicting
from a given set of variables. Examples of Supervised Machine Learning
Algorithms are KNN, Decision Trees, and Logistic Regression etc.

 

In unsupervised machine learning
algorithm, there are no input or output variables, instead it is used to figure
out useful patterns and structure from the data. Some of the examples of
unsupervised learning algorithm are K-means algorithm, Apriori Algorithm etc.

 

 In the Reinforcement machine learning
algorithm, the machines are trained to make decisions by calculating how good
or bad a situation is. Examples of this type of algorithms include Markov
Decision Process etc.

 

 The
Machine learning approaches and algorithms are essentially data driven and they
are proposed and enforced to make decisions or predictions from the possessed
data (Rutta, Scioscia, Losetoa, Pintoa & Di Sciascioa, 2017). Every machine
learning algorithm is based on certain values of parameters and for every
machine learning algorithm, there will be a distinct way of understanding it.
If, for interpreting a problem of classification, a machine learning algorithm
makes use of a divergent set of parameters, then the validity of the
classification of the problem will differ greatly in each case. One of the
major difficulties in Machine learning is to search the most satisfactory pair
of values for parameters for an algorithm to solve a problem in order to get
the best performance metrics. Therefore, to get the best result for solving a
problem, the best suitable pair of parameter values must be used.

 

A key feature that makes Machine
learning much more appealing is that much explicit programming is not required
to achieve meaningful understanding of data by Machine learning unlike other
technologies.

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