(untitled) (Phoenix: International Multidisciplinary Research Journal ( Peer reviewed High Impact Journal ), 2022)
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.