Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science 2021, 2, doi:10.1007/s42979-021-00592-x.
Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science 2021, 2, doi:10.1007/s42979-021-00592-x.
Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science 2021, 2, doi:10.1007/s42979-021-00592-x.
Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science 2021, 2, doi:10.1007/s42979-021-00592-x.
Abstract
In the current age of the Fourth Industrial Revolution ($4IR$ or Industry $4.0$), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding real-world applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world applications areas, such as cybersecurity, smart cities, healthcare, business, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for not only the application developers but also the decision-makers and researchers in various real-world application areas, particularly from the technical point of view.
Keywords
machine learning; deep learning; artificial intelligence; data science; data-driven decision making; predictive analytics; intelligent applications;
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
