MACHINE LEARNING ALGORITHMS
DOI:
https://doie.org/10.5281/7b879r50Keywords:
Machine Learning Algorithms, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Linear Regression, Decision Trees, Support Vector Machines, K means Clustering, Hierarchical Clustering, Principal Component Analysis, Deep Learning, Explainable AI, Data Analysis, Applications of Machine Learning.,,Abstract
Machine learning algorithms have become a fundamental part of modern data analysis and
decision-making processes across various domains. This paper provides an overview of key
machine learning algorithms, their applications, and underlying principles. The algorithms
covered include supervised learning (e.g., linear regression, decision trees, support vector
machines), unsupervised learning (e.g., k-means clustering, hierarchical clustering, principal
component analysis), and reinforcement learning. We discuss their strengths, weaknesses, and
real-world use cases. Additionally, we explore emerging trends and challenges in the field of
machine learning, such as deep learning and explainable AI. Understanding these algorithms is
essential for harnessing the power of machine learning in today's data-driven world.
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