Название: Machine Learning for Real World Applications
Автор: Dinesh K. Sharma, H.S. Hota, Aaron Rasheed Rababaah
Издательство: Springer
Год: 2024
Страниц: 315
Язык: английский
Формат: pdf (true)
Размер: 24.9 MB

This book provides a comprehensive coverage of Machine Learning techniques ranging from fundamental to advanced. The content addresses topics within the scope of the book from the ground up, providing readers with a trustworthy source of theoretical and technical learning content. The book emphasizes not only the theoretical features but also their practical and implementation aspects in real-world applications. These applications are crucial because they provide comprehensive experimental work that supports the validity of the offered approaches as well as clear instructions on how to apply such models in comparable and distinct settings and contexts. Furthermore, the chapters shed light on the problems and possibilities that researchers might use to direct their future research efforts. The book is beneficial for undergraduate and postgraduate students, researchers, and industry personnel.

The book’s foundation recognizes that Machine Learning, a significant field within Artificial Intelligence (AI), has become a cornerstone technology influencing current and future trends in industry, healthcare, finance, security, and much more. Its growing prominence is evident in the attention it has received from the research and scientific communities, especially in the realm of Deep Learning, where significant advances have pushed the boundaries of intelligent models to new heights. This comprehensive volume covers an impressive array of topics, providing readers with a thorough journey through fundamental and advanced machine learning concepts. The chapters are thoughtfully crafted, addressing subjects within the book’s scope from the ground up. This ensures that readers, whether they are undergraduate and postgraduate students, researchers, or industry professionals, will find a reliable source of theoretical and technical learning content.

One of the book’s unique strengths is its emphasis on practical implementation. While the theoretical aspects are essential in building a solid foundation, it is in the application of Machine Learning models to real-world scenarios that their true potential is realized. Each chapter presents practical use cases, offering readers valuable insights into how these models can be implemented effectively to address complex challenges in healthcare, finance, agriculture, security, and beyond. The authors have demonstrated exceptional expertise in their respective fields, presenting cutting-edge research and innovative applications of Machine Learning algorithms. These applications carry significant weight, serving as rich experimental work supporting the presented methods’ validity. Moreover, the detailed guidelines for implementing these models in similar and different scenarios ensure that readers can translate theoretical knowledge into practical solutions.