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As machine learning continues to revolutionize diverse industries, this comprehensive review paper aims to provide an overview of the current landscape of machine learning techniques and their applications. The paper delves into the fundamentals of machine learning, highlighting key algorithms such as decision trees, neural networks, and clustering methods. Recent advancements, including deep learning, transfer learning, and explainable AI, are explored in depth, showcasing their potential to reshape various domains. The review further examines the extensive spectrum of applications, ranging from natural language processing to healthcare and autonomous vehicles. Challenges like bias and interpretability, along with ethical considerations, are addressed, emphasizing the responsibility of researchers in guiding the ethical development of AI. A comparative analysis of machine learning approaches offers insights into their comparative strengths and weaknesses. Methodology details the systematic literature review process undertaken. In conclusion, this review paper synthesizes the multifaceted field of machine learning, presenting a valuable resource for researchers, practitioners, and policymakers navigating the dynamic landscape of AI.
Keywords:
Machine Learning, Artificial Intelligence, Algorithms, Deep Learning, Transfer Learning, Neural Networks, Clustering Methods, Applications, Natural Language Processing
Cite Article:
"MACHINE LEARNING - A REVIEW", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 8, page no.677 - 683, August-2023, Available :http://www.ijrti.org/papers/IJRTI2308109.pdf
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000205045
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator