A Review on Machine Learning & It’s Algorithms
Nipun Jain1, Rajeev Kumar2

1Nipun Jain, Department of Electrical Engineering, IIT Roorkee, Roorkee, India.
2Rajeev Kumar, Department of Electrical Engineering, IIT Roorkee, Roorkee, India.

Manuscript received on 10 October 2022 | Revised Manuscript received on 14 October 2022 | Manuscript Accepted on 15 November 2022 | Manuscript published on 30 November 2022 | PP: 1-5 | Volume-12 Issue-5, November 2022 | Retrieval Number: 100.1/ijsce.E35831112522 | DOI: 10.35940/ijsce.E3583.1112522
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: A Machine learning is important because it gives us accurate predictions based on data. It can teach computers to perform complex tasks without any human intervention. Machine learning can analyze complex blocks of data. Machine learning enables entrepreneurs and businesses to quickly recognize potential business opportunities and risks. Businesses that rely solely on large amounts of data are using machine learning as the best way to analyze data and build models. Machine learning is not only considered as the backbone of artificial intelligence, but machine learning also plays a significant role in the development and advancement of artificial intelligence. Using algorithms to solve classification problems with different sets of parameters yields dramatically different classification accuracies. The machine learning challenge of finding the most appropriate parameter values for algorithms that best solve technical problems related to performance metrics. In this paper, the author discussed various types of machine learning such as supervised, unsupervised and reinforcement machine learning. The main emphasis is on supervised machine learning such as classification and regression using various machine learning algorithms such as Decision Tree, Naïve Bayes, K-Nearest Neighbor, Random Forest and SVM Classifier. The author explains all classification-based algorithms well with examples and diagrams. The authors also mention applications or domain areas where these classification algorithms can be used.
Keywords: Supervised Learning, K-Nearest Neighbor, SVM, Random Forest, Decision Tree, Naïve Bayes Classifier.
Scope of the Article: Machine Learning