Introduction to Artificial Intelligence and Machine Learning Algorithms: A Review
Abstract views: 383
/ 
PDF downloads: 130
Keywords:
Artificial Intelligence, Machine Learning, Python, AI, ML, Linear Regression, Polynomial regression, Classification, Random Forests, Algorithms, Random Forest algorithmAbstract
This paper provides an extensive overview of Artificial Intelligence (AI) and Machine Learning
(ML) algorithms and their interdisciplinary nature to revolutionize any field, discussing their evolution,
fundamentals, applications, and challenges. AI and ML technologies have revolutionized various industries,
driving innovation and efficiency across various domains. This paper explores the multidisciplinary nature
of AI and ML, emphasizing their significance in analyzing large datasets, making predictions, and
automating decision-making processes. It traces the historical milestones of AI, from Alan Turing's
pioneering work to the rise of deep learning and neural networks. The fundamentals of ML algorithms,
including supervised, unsupervised, and reinforcement learning, are explained along with their practical
applications in healthcare, finance, engineering, transportation, and e-commerce. Moreover, this paper
addresses key challenges that are faced by AI and ML technologies, such as uncertainty, algorithm selection
complexity, and overfitting, highlighting the importance of ongoing research and interdisciplinary
collaboration in addressing these challenges. The ultimate goal of this paper is to reinforce the paradigm
altering potential of AI and ML technologies in shaping the future of intelligent AI and ML driven systems
and smart societies.
Downloads
References
SRINATH, K. R. (2017). "PYTHON – THE FASTEST GROWING PROGRAMMING LANGUAGE". INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET), VOLUME: 04 (ISSUE: 12), 354-357. HTTPS://WWW.IRJET.NET/ARCHIVES/V4/I12/IRJET-V4I1266.PDF
HATON, J. P. (2006). "A BRIEF INTRODUCTION TO ARTIFICIAL INTELLIGENCE". IFAC PROCEEDINGS, VOLUME: 39 (ISSUE: 04), 8-16. HTTPS://DOI.ORG/10.3182/20060522-3-FR-2904.00003
SODHI, PINKY AND AWASTHI, NAMAN AND SHARMA, VISHAL,. "INTRODUCTION TO MACHINE LEARNING AND ITS BASIC APPLICATION IN PYTHON". (JANUARY 6, 2019). PROCEEDINGS OF 10TH INTERNATIONAL CONFERENCE ON DIGITAL STRATEGIES FOR ORGANIZATIONAL SUCCESS, AVAILABLE AT SSRN: HTTPS://SSRN.COM/ABSTRACT=3323796 OR HTTP://DX.DOI.ORG/10.2139/SSRN.3323796
MAULUD, DASTAN & MOHSIN ABDULAZEEZ, ADNAN. (2020). "A REVIEW ON LINEAR REGRESSION COMPREHENSIVE IN MACHINE LEARNING". JOURNAL OF APPLIED SCIENCE AND TECHNOLOGY TRENDS. 1. 140-147. 10.38094/JASTT1457.
QUINLAN, J.R. "INDUCTION OF DECISION TREES". MACH LEARN 1, 81–106 (1986). HTTPS://DOI.ORG/10.1007/BF00116251
IZZA, RODRIGO, IGNATIEV, ALEXEY, & MARQUES-SILVA, JOAO. "ON EXPLAINING DECISION TREES". ARXIV PREPRINT ARXIV:2010.11034 (2020). DOI: 10.48550/ARXIV.2010.11034
YANG, Y., MORILLO, I. G., & HOSPEDALES, T. M. (2018). "DEEP NEURAL DECISION TREES". ARXIV, ABS/1806.06988. [ONLINE]. AVAILABLE: [HTTPS://API.SEMANTICSCHOLAR.ORG/CORPUSID:49317359](HTTPS://API.SEMANTICSCHOLAR.ORG/CORPUSID:49317359)
LIAW, ANDY & WIENER, MATTHEW. (2001). "CLASSIFICATION AND REGRESSION BY RANDOMFOREST". FOREST. 23. (PDF) CLASSIFICATION AND REGRESSION BY RANDOMFOREST (RESEARCHGATE.NET)
SEGAL, MARK. (2003). "MACHINE LEARNING BENCHMARKS AND RANDOM FOREST REGRESSION". TECHNICAL REPORT, CENTER FOR BIOINFORMATICS & MOLECULAR BIOSTATISTICS, UNIVERSITY OF CALIFORNIA, SAN FRANCISCO. (PDF) MACHINE LEARNING BENCHMARKS AND RANDOM FOREST REGRESSION (RESEARCHGATE.NET)
SARKER, IQBAL. (2021). MACHINE LEARNING: ALGORITHMS, REAL-WORLD APPLICATIONS AND RESEARCH DIRECTIONS. SN COMPUTER SCIENCE. 2. 10.1007/S42979-021-00592-X.
