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Machine learning algorithms review
- 1 College of Science, Kean University, Union, New Jersey, U.S. 07083
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Abstract
Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machine learning and its algorithms. It will examine different types of machine learning models and introduce their most popular algorithms. The methodology of this paper is a literature review, which examines the most commonly used machine learning algorithms in the current field. Such algorithms include Nave Bayes, Decision Tree, KNN, and K-Mean Cluster. Nowadays, machine learning is everywhere and almost everyone using a technology product is enjoying its convenience. Applications like spam mail classification, image recognition, personalized product recommendations, and natural language processing all use machine learning algorithms. The conclusion is that there is no single algorithm that can solve all the problems. The choice of the use of algorithms and models must depend on the specific problem.
Keywords
Machine learning, Supervised learning, Unsupervised learning, Reinforcement Learning, Neural networks
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Cite this article
Ling,Q. (2023). Machine learning algorithms review. Applied and Computational Engineering,4,91-98.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
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