A Comprehensive Study of Machine Learning Models - Types, Examples, and Use Cases

Author(s)	Chandra mouli Yalamanchili
Country	United States
Abstract	Machine learning (ML) has evolved into a strong field that enables machines to learn from data and make decisions without programming. Different requirements led to the evolution of various models and classifications depending on how they are built and what they are solving. This paper explores multiple machine learning models, their classifications, and use cases. This paper aims to help readers understand how different types of ML models solve distinct problems such as regression, classification, clustering, association, anomaly detection, and reinforcement learning use cases. The paper also categorizes ML models based on their supervised, unsupervised, semi-supervised, and reinforcement nature, offering a comprehensive list of algorithms within each classification. This paper also includes sample Python code examples for key models to demonstrate practical usage and execution to provide more context to the readers interested in implementation.
Keywords	Machine Learning; Supervised Learning; Unsupervised Learning; Semi-supervised Learning; Reinforcement Learning; Regression; Classification; Clustering; Anomaly Detection; Association Rules.
Field	Engineering
Published In	Volume 5, Issue 1, January-February 2023
Published On	2023-02-09
DOI	https://doi.org/10.36948/ijfmr.2023.v05i01.41033
Short DOI	https://doi.org/g9dhjm

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International Journal For Multidisciplinary Research