Ways of common Machine learning and Common algorithmic approaches in it
Author(s):
Article Type:
Research/Original Article (بدون رتبه معتبر)
Abstract:
Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning in general is to understand the structure of data and fit that data into models that people can understand and use. Today, every technology user has benefited from machine learning. Facial recognition technology allows social media platforms to help users tag and share photos of their friends. Optical Character Recognition (OCR) technology converts text images into animated type. Recommendation engines, using machine learning, suggest what movies or TV shows to watch next, based on user preferences. Self-driving cars that rely on machine learning to navigate may soon be available to consumers. Machine learning is a constantly developing field. For this reason, there are considerations to keep in mind when working with machine learning methods or analyzing the impact of machine learning processes. In this article, common machine learning methods of supervised and unsupervised learning and common algorithmic approaches in machine learning. including k-nearest neighbor algorithm, decision tree learning and deep learning. His findings showed. With the expansion of the scope and fields of study and the use of machine learning in different fields, depending on the changing needs and complexity of different problems, different types of machine learning algorithms have emerged, each of which has its own efficiency and applications. . So you can choose the algorithm that best suits your business needs. In some cases, experts use a combination of these algorithms because one algorithm alone may not be able to solve a particular problem.
Language:
Persian
Published:
Journal of Intelligent Knowledge Exploration and Processing, Volume:4 Issue: 12, 2024
Pages:
76 to 87
https://www.magiran.com/p2751570