EMPIRICAL APPROACHES IN MACHINE LEARNING

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Machine learning is a science founded and developed in the 1950s as a subfield of artificial intelligence. Machine learning is the scientific study of algorithms and statistical models developed to automate machines just as humans do. Machine learning techniques are being used in various applications such as search engines, spam filtering, recognition of faces, texts, signatures and speech, classification of documents, social network analysis, health record monitoring, and tutoring systems to identify students' abilities and weaknesses, etc. This study provides a brief overview of the extensive applications of machine learning and a glimpse of what the future holds.

References

1. Batta Mahesh, Machine Learning Algorithms – A Review, International Journal of Science and Research (IJSR), Volume 9, Issue 1, January 2020. https://www.ijsr.net/ get_count.php ?paper_id =ART20203995.

2. Taiwo Ayodele, Machine Learning Overview, New Advances in Machine Learning (Book), February 2010. https://www.researchgate.net/publication/221907649_ Machine_Learning _Overview.

3. Ahmad Hammoudeh, A Concise Introduction to Reinforcement Learning, February 2018. https://www.researchgate.net/publicatio n/323178749_A_Concise_Introduction_to_Reinforcement_Learning.

Keywords

Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning.