Machine Learning as a Modern Approach to Data Analysis

Abstract

Relevance. Given the widespread digitalization and automation of production processes, information technology in modern society is becoming increasingly important. Methods and methods used earlier become unsuitable in the current conditions of economic development. Against this background, machine learning is of particular importance, designed to analyze information faster and more efficiently than a person. Machine learning is an emerging field of computational algorithms designed to mimic human intelligence and discover patterns in data. Today it is one of the fastest growing technical fields, lying at the intersection of computer science, statistics and business. Machine learning is already being effectively used to solve various analytical and optimization problems.The purpose of the article is to study machine learning from a theoretical point of view and evaluate the effect of its application.Objectives: explore the phenomenon of "Big Data" as an impetus to the use of machine learning; consider the history of the origin of machine learning; give an interpretation of this concept; describe the basic principles of machine learning; assess interest in this area; study specific cases of successful implementation of machine learning.Methodology. During scientific research, empirical, theoretical, statistical methods and methods of graphical representation were used.Results. Theoretical aspects of machine learning were studied, the indicators of the popularity of this topic both in the scientific field and in business were studied. Examples of the positive effect of the use of machine learning are shown.Conclusions. The paper emphasizes the importance of the topic under consideration, considering the latest trends, justifies the benefits of using modern methods of analysis.