An Overview of Machine Learning: Process, Algorithms, How to choose an algorithm, Applications, Benefits and Challenges
Subject Areas :
Emel Sayah
1
,
امیر عباس شجاعی
2
,
ali ali akbari
3
,
Mehdi HajiRezaei
4
,
Hamid Tohidi
5
1 - Faculty of Industrial Engineering, Islamic AZAD University, South Tehran Branch,Tehran, Iran
2 - دانشگاه تهران جنوب
3 - Islamic Azad University, South Tehran Branch, Department of Industrial engineering
4 - Faculty of Communications, Transportation and Logistics, Islamic Azad University, South Tehran Branch
5 - Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Keywords: Machine learning, artificial intelligence, machine learning process, machine learning algorithms,
Abstract :
Machine learning algorithms are computational models that allow computers to understand patterns and make predictions or decisions based on data without explicit programming. These algorithms form the foundation of modern artificial intelligence and are used in various applications such as image and speech recognition, natural language processing, recommender systems, fraud detection, and autonomous vehicles. This study aims to provide a brief overview of machine learning by discussing the development of machine learning to date and then examining the machine learning process, its various algorithms, and the method of selecting algorithms. It also addresses the applications, challenges, and benefits of machine learning. Overall, this paper intends to serve as a reference point for academic and industry professionals as well as for decision-makers in various real-world situations and application domains. The present research methodology is based on qualitative analysis in which various literature based on machine learning is reviewed. The results show that machine learning is poised to create a major technological revolution and is one of the fastest-growing technologies used to date, and it is constantly evolving. Machine learning is a high-risk, high-reward technology.
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