A Survey on Machine Learning

	International Journal of P2P Network Trends and Technology (IJPTT)
	© 2019 by IJPTT Journal
	Volume-9 Issue-1
	Year of Publication : 2019
	Authors : R.Anil Kumar, Shaik ZakirAhamed and S.Syed Basha
	DOI :  10.14445/22492615/IJPTT-V9I1P403

Citation

MLA Style: R.Anil Kumar, Shaik ZakirAhamed and S.Syed Basha "A Survey on Machine Learning" International Journal of P2P Network Trends and Technology 9.1 (2019): 9-12.

APA Style:R.Anil Kumar, Shaik ZakirAhamed and S.Syed Basha (2019). A Survey on Machine Learning. International Journal of P2P Network Trends and Technology, 9(1), 9-12.

Abstract

In the decades, Machine Learning (ML) has advanced from the undertaking of few PC lovers misusing the likelihood of PCs figuring out how to play amusements, and a piece of Mathematics (Statistics) that only sometimes thought to be computational methodologies, to an autonomous research discipline that has not just given the fundamental base to measurable computational standards of learning techniques, yet in addition has created different algorithms that are normally utilized for content translation, design acknowledgment, and a numerous other business purposes and has prompted a different research enthusiasm for information mining to distinguish concealed regularities or inconsistencies in social information that developing by second. This paper centers around clarifying the idea and development of Machine Learning, a portion of the prevalent Machine Learning algorithms and endeavor to look at three most well known algorithms dependent on some essential ideas. Sentiment140 dataset was utilized and execution of every algorithms as far as preparing time, expectation time and precision of forecast have been recorded and looked at.

References

[1] ?Machine Learning?. [Online]. Available:https://en.wikipedia.org/wiki/Machine_learning
[2] T.M. Mitchel, The Discipline of Machine Learning, CMU-ML-06-108, 2006.
[3] Cour, T. and Sapp, B. and Taskar, B. Learning from partial labels, Journal of Machine Learning Research, Volume 12, 1501-1536 2012.
[4] J.Baxter. A model of inductive bias learning. Journal of Artificial Intelligence Research, 12:149–198, 2000.
[5] ?Types of Machine Learning Algorithms? Available:https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861?gi=a9ac49994031

Keywords
Machine Learning, Algorithm, Applications.