Algorithmic Approaches, Practical Implementations and Future Research Directions in Machine Learning

EasyChair Preprint 11924

Abstract

This thorough review article explores the complex field of machine learning, providing a detailed look at algorithms, practical applications, and the changing research paths that will continue to develop this revolutionary field. The story begins with a perceptive introduction that highlights the significance and application of machine learning across a range of industries. The study demonstrates the significant influence of machine learning on several areas, including healthcare, banking, marketing, and autonomous cars, with a smooth transition into practical applications. The practical applicability of machine learning is demonstrated through in-depth case studies, highlighting the technology's ability to spark creativity and solve complicated, real-world problems.

Recognizing the difficulties that come with machine learning, the paper addresses a variety of topics, from ethical implications to worries about data quality. This open examination lays the groundwork for ethical AI development by forcing practitioners and academics to confront possible hazards and negotiate the morally challenging aspects of algorithmic decision-making. This review provides a thorough resource for scholars, practitioners, and policymakers, promoting a greater comprehension of the nuances of the field and laying out a roadmap for ethical and significant machine learning breakthroughs.

Keyphrases: Artificial Intelligence, Data Integrity, Generative Adversarial Networks (GANs), deep learning, machine learning, neural networks, optimization methods, robustness

@booklet{EasyChair:11924,
  author    = {Vaishali Sharma and Deepank Sharma and Sanjeev Kumar Punia},
  title     = {Algorithmic Approaches, Practical Implementations and Future Research Directions in Machine Learning},
  howpublished = {EasyChair Preprint 11924},
  year      = {EasyChair, 2024}}