Reliable Algorithms for Machine Learning Models: Implementation Research in Data Science
Kajal Singh1, Anukriti Mukherjee2
1Kajal Singh*, Business Intelligence Analyst, Schneider Electric, Bangalore, India.
2Anukriti Mukherjee, Programmer Analyst, Cognizant Technology Solutions, Kolkata, India.
Manuscript received on February 16, 2022. | Revised Manuscript received on February 21, 2022. | Manuscript published on March 30, 2022. | PP: 102-106 | Volume-10 Issue-6, March 2022. | Retrieval Number: 100.1/ijrte.F68710310622 | DOI: 10.35940/ijrte.F6871.0310622
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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: Machine Learning generates programs that make predictions and informed decisions about complex problems in an efficient and reliable way. These ML programs autonomously test solutions against the dataset to find the best fit for the problem. The paper aims to review the ML algorithms that develop prediction models by utilizing training dataset and known output. The paper also focuses on ML principles, algorithms, approaches, and applications for Supervised, Unsupervised, and Reinforcement learning that can perform tasks without being explicitly programmed for it. Completely opposite to rule-based programming, the machine learning paradigm uses examples of real data sets and pre-process it before providing the desired outputs based on these examples. In the case of more involved and complex tasks, it can be challenging for humans to explicitly program the models. On the other hand, it can be more effective to help the machines develop the algorithms for advanced tasks. This paper will also present the trending real-world applications of Machine Learning in Image Recognition and Biomedicine. Additionally, it will provide a background analysis of machine learning and related fields of data science.
Keywords: Machine Learning (ML), Supervised Learning, Unsupervised Learning, Reinforcement Learning, KNN, K- Means Clustering.
Scope of the Article: Machine Learning (ML)