LINEAR ALGEBRA IN MACHINE LEARNING

Linear algebra is a continuous form of mathematics as it allows you to model-natural phenomena and compute them essentially. It is the study of vectors, matrices, planes mapping and lines required for linear transformation which forms the essence of machine learning and also has the potential to reduce time complexities in other words, linear algebra is the basic mathematics of data.  Linear Algebra is the pillar of machine learning as it not only provides useful shortcuts for describing data as well as operations on data that we need to perform in machine learning methods. It is not magic, it is not trying to exclusive so as a first step we need to think linear algebra as a shortcut language or notation to make describing some operations dense. Many libraries requires the basic knowledge of linear algebra and as a machine learning practitioner one must have the basic understanding of linear algebra to attain mastery in this field . Thus, linear algebra plays a vital role in machine learning as it enables ML algorithms to run on a huge number of datasets.