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An Analytical Study on Machine Learning Techniques

An Analytical Study on Machine Learning Techniques

Law Kumar Singh, Pooja, Hitendra Garg, Munish Khanna, Robin Singh Bhadoria
Copyright: © 2021 |Pages: 21
ISBN13: 9781799858768|ISBN10: 1799858766|ISBN13 Softcover: 9781799872306|EISBN13: 9781799858775
DOI: 10.4018/978-1-7998-5876-8.ch007
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MLA

Singh, Law Kumar, et al. "An Analytical Study on Machine Learning Techniques." Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications, edited by Niaz Chowdhury and Ganesh Chandra Deka, IGI Global, 2021, pp. 137-157. https://doi.org/10.4018/978-1-7998-5876-8.ch007

APA

Singh, L. K., Pooja, Garg, H., Khanna, M., & Bhadoria, R. S. (2021). An Analytical Study on Machine Learning Techniques. In N. Chowdhury & G. Chandra Deka (Eds.), Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications (pp. 137-157). IGI Global. https://doi.org/10.4018/978-1-7998-5876-8.ch007

Chicago

Singh, Law Kumar, et al. "An Analytical Study on Machine Learning Techniques." In Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications, edited by Niaz Chowdhury and Ganesh Chandra Deka, 137-157. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-5876-8.ch007

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Abstract

The last few months have produced a remarkable expansion in research and deep study in the field of machine learning. Machine learning is a technique in which the set of the methods are used by the computers to make prediction, improve prediction and behavior prediction based on dataset. The learning techniques can be classified as supervised and unsupervised learning. The focus is on supervised machine learning that covers all the predictions problem for which we had the dataset in which the outcome is already known. Some of the algorithm like naive bayes, linear regression, SVM, k-nearest neighbor, especially neural network have gain growth in this area. The classifiers of machine learning are completely unconstrained with the assumptions of statistical and for that they are adapted by complex data. The authors have demonstrated the application of machine learning techniques and its ethical issues.

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