Authors :
Jasmin Praful Bharadiya
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://t.ly/iOpj
DOI :
https://doi.org/10.5281/zenodo.8020795
Abstract :
Machine Learning (ML) is a research area
that has developed over the past few decades as a result
of the work of a small group of computer enthusiasts
who were interested in the idea of computers learning to
play video games and from a branch of mathematics
called statistics that hardly ever took computational
methods into consideration. The development of a large
number of algorithms that are frequently used for text
interpretation, pattern recognition, and a variety of
other business purposes has sparked clear research
interest in data mining to find hidden regularities or
irregularities in data. data. data. data. social data is
growing by the second This article describes the idea and
history of machine learning and contrasts the three most
popular machine learning algorithms using some
fundamental ideas. The Sentiment140 dataset has been
used to demonstrate and evaluate the efficiency of each
method in terms of training time, prediction time, and
prediction accuracy. Machine learning algorithms have
become indispensable tools in analyzing complex
datasets and extracting valuable insights. Among the
myriad of algorithms available, one particular technique
has gained widespread popularity due to its versatility
and effectiveness. This comprehensive review aims to
delve into the efficacy of this popular machine learning
algorithm by offering a comprehensive analysis of its
underlying principles, diverse applications, notable
strengths, and inherent limitations.
Keywords :
Algorithm, Supervised Learning, Unsupervised Learning, Regression, Deep Learning and Support Vector Machines.
Machine Learning (ML) is a research area
that has developed over the past few decades as a result
of the work of a small group of computer enthusiasts
who were interested in the idea of computers learning to
play video games and from a branch of mathematics
called statistics that hardly ever took computational
methods into consideration. The development of a large
number of algorithms that are frequently used for text
interpretation, pattern recognition, and a variety of
other business purposes has sparked clear research
interest in data mining to find hidden regularities or
irregularities in data. data. data. data. social data is
growing by the second This article describes the idea and
history of machine learning and contrasts the three most
popular machine learning algorithms using some
fundamental ideas. The Sentiment140 dataset has been
used to demonstrate and evaluate the efficiency of each
method in terms of training time, prediction time, and
prediction accuracy. Machine learning algorithms have
become indispensable tools in analyzing complex
datasets and extracting valuable insights. Among the
myriad of algorithms available, one particular technique
has gained widespread popularity due to its versatility
and effectiveness. This comprehensive review aims to
delve into the efficacy of this popular machine learning
algorithm by offering a comprehensive analysis of its
underlying principles, diverse applications, notable
strengths, and inherent limitations.
Keywords :
Algorithm, Supervised Learning, Unsupervised Learning, Regression, Deep Learning and Support Vector Machines.
