Imperative Linear Algebra For Data Science With R-Software

Authors

  • R. Nagarathinam, R Punitha, Mrs.M.Nazreen Banu

DOI:

https://doi.org/10.47750/pnr.2022.13.S10.174

Abstract

Data science and machine learning are built on linear algebra. Machine learning and data science make extensive use of linear algebra, a branch of Mathematics. Machine learning relies heavily on linear algebra. Matrix representations are commonly used in machine learning models. Using linear algebra in data science means regularizing, reducing to dimensions, recognizing images, learning algorithms, and analyzing images. Many data science algorithms are based on linear algebra. This article will cover three uses of linear algebra in three different data science domains. We will discuss loss functions from the perspective of machine learning, and image convolution from the perspective of computer vision. Any prospective data scientist must learn R since it is a very strong language designed specifically for data analysis and data visualisation. With linear algebra, R is extremely useful. It has built-in data types like matrices and vectors.

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Published

2022-12-31 — Updated on 2022-12-31

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Articles

How to Cite

Imperative Linear Algebra For Data Science With R-Software . (2022). Journal of Pharmaceutical Negative Results, 1482-1489. https://doi.org/10.47750/pnr.2022.13.S10.174