Machine Learning Algorithms for Advanced Data Analytics

Abstract

In the rapidly evolving field of data analytics, machine learning (ML) has emerged as a critical tool for extracting valuable insights from complex datasets. This paper explores the role of machine learning algorithms in advanced data analytics, highlighting key methodologies, applications, and challenges. We examine various ML algorithms, including supervised, unsupervised, and reinforcement learning, and their relevance to tasks such as classification, regression, clustering, and anomaly detection. Additionally, the paper discusses the integration of machine learning with big data technologies, emphasizing the importance of scalable frameworks like Apache Spark and TensorFlow in managing and processing large datasets. Ethical considerations, including bias in ML models and data privacy, are also addressed, underscoring the need for responsible AI practices in analytics. By exploring the intersection of machine learning and data analytics, this paper aims to provide a comprehensive overview of how these technologies are shaping the future of decision-making across industries.