Machine learning(ML) is the field of Computer Science that uses different models for prediction, classification, and analysis. Machine learning is a Mathematical model prerequisite by aggregation of linear algebra, statistics, calculus, and probability. In this paper, the art of mathematics in machine learning by referring to a different research article from repositories is identified and the need for mathematics for building machine learning models to perform calculations operations such as matrix manipulation of the data for which linear algebra is preferred. To build an uncertainty model in machine learning we prefer probability for learning model and create analysis on a given training dataset for creative prediction. ML is intrinsically data-dependent and driven. The data captured from different sources are improper and consist of much invalid information to perform the statistical analysis we need to perform data preprocessing and validation during which calculus plays a vital role in making the data ready with minimum error. The contribution of the paper is to identify the apogee of mathematical rules required for building an appropriate model.

Machine Learning, Statistics, Probability, Calculus, Mathematical Model