THE BIG DATA AND MACHINE LEARNING

Abstract

The aim of this paper is to present advanced methods for the search for new knowledge contained in BIG DATA, huge, growing datasets and technology. To that end, machine learning is illuminated, a science that trains computers to analyze data and solve tasks without having to be explicitly programmed to do it. Sub-areas of machine learning, as part of artificial intelligence, are presented to solve these problems. The areas of classical machine learning are supervised learning (classification and regression), unsupervised learning (clustering, pattern search, dimensionality reduction), the support machine vector, and the decision tree. The areas of modern machine learning are enhanced learning, ensemble methods, neural networks and deep learning, and Bayesian networks, as special, additional sub-fields and methods in the field of machine learning. The work and results of this paper are significant because the described machine learning methods are inevitable, with the emphasis that they will increase in importance, as it is a realistic expectation that BIG DATA technology will evolve, with a tendency to absorb new data from many sources, creating new sources of knowledge. Web 2.0, with its Google apps, blogs, wikipedia, social networks, Facebook, folksonomies, video sharing online, Web mobile apps, is just one of these inexhaustible sources of data. Knowledge is obtained from data analysis.