Machine Learning: Algorithms, Models, and Applications

TL;DR

This paper reviews recent advances in machine learning algorithms and models, highlighting their applications across various fields like healthcare, finance, and automation, emphasizing design, optimization, and deployment.

Contribution

It presents innovative research works and real-world applications of machine learning and deep learning, illustrating their design, optimization, and deployment in diverse domains.

Findings

Advances in reinforcement learning, NLP, vision, and speech processing.

Applications in stock trading, healthcare, and automation.

Guidance for researchers and practitioners in deploying ML models.

Abstract

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.