INTRODUCTION
The area of mathematics known as "linear algebra" deals with linear equations and how they are represented in vector spaces and through matrices. The study of vectors and linear functions is what linear algebra is, to put it simply. Linear algebra involves the study of matrices, determinants, linear transformations, vector spaces, and subspaces and uses closed vectors that operate under addition and scalar multiplication. It enables us to do mathematical operations and comprehend geometric notions in greater dimensions. Nearly all branches of mathematics, including geometry and functional analysis, are based on linear algebra. Understanding its ideas is essential for comprehending the theory underlying data science. Data science is a branch of study that combines subject matter knowledge, programming abilities, and a working understanding of mathematics and statistics to draw out valuable insights from data.
Data scientists use machine learning algorithms to analyses data from a variety of sources, including text, images, videos, and audio, to create artificial intelligence systems that can carry out activities that often require human intellect. To study and analyze real-world phenomena using data, "data science" is a concept that combines statistics, data analysis, informatics, and their related methodologies. In the context of mathematics, statistics, computer science, information science, and domain knowledge, it makes use of methods and theories from a variety of domains. A data scientist is a person who writes programming code and uses statistical expertise in conjunction with it to derive insights from data
Data scientists may be able to avoid using linear algebra for a while, but not for very long. Here are several ways that linear algebra can help with computer vision and machine learning. Few people think of linear algebra when discussing data science in general or specific subfields like machine learning or computer vision. Because the modern tools we use to perform data science algorithms do a great job of disguising the underlying math that makes things function, linear algebra is sometimes overlooked. In data science, linear algebra is used.
When building a machine learning model, it will most probably dealing with large data sets having multiple rows and columns. These are nothing but matrices when you spilt your dataset into training and testing data, you are performing operations on these matrices. Matrices are the key data structure in linear algebra and it deals with the various operations performed on matrix, including row and column transformations, transpose of a matrix, addition or scalar multiplication in matrices
