A Model Based Linear Algebraic Approach for Machine Learning

Abstract

Linear algebra is sub field of mathematics and contains matrix's, Operations on dataset, Vectors. Linear algebra is core base for purely statistics and mathematics person to achieve goals with basic logics of linear algebra using machine learning. As a machine learning aspirant, the aspirant should be best at linear algebra to work on creating a new module or bundle. In other areas, such as statistics, engineering, research, linear algebra plays an important role and has strongest impact Although solving any problem mathematically with linear algebra is crucial for experienced engineers, linear algebra is crucial in machine learning, and in the case of any practical implementation of linear algebra, it is quite probable that the technical person may not have mathematical knowledge. Easier implementation is provided by linear algebra. Coronavirus disease (COVID-19) has raised urgent questions regarding minimizing and improving effective analytical strategies for analyzing information obtained from standardized protocols. In order to understand what quality of care interventions are the most successful and to test them as quickly as possible, it becomes necessary to evaluate available data immediately. It is necessary to integrate and clean up data from large multi-center hospitals and needs sophisticated data processing. To collect and store their data in a standardized manner, case report forms (CRF) for patients with suspected or confirmed COVID-19 are required. The goal of this study is to determine if it is possible to avoid or postpone local outbreaks of COVID-19 by travel restrictions from abroad. The problem statement is that we are unable to gather clear suspicious data from big data of covid-19 patients, and during the corona outbreak it becomes difficult to distinguish symptotic and asymptotic patients. The number of beds required or ventilators needed for COVID-19 cases at this point is difficult to estimate.