Machine Learning Crash Course for Engineers

Machine Learning Crash Course for Engineers is a reader-friendly introductory guide to machine learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement machine learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of machine learning quickly.

A concise guide to the basics of algorithms, building models, and performance evaluation Offers highly illustrated, step-by-step guidelines with Python programming examples Provides examples and exercises related to signal and image processing, energy systems, and robotics

Autorentext

Eklas Hossain, Ph.D., is an Associate Professor in the Department of Electrical and Computer Engineering at Boise State University, Idaho. He has worked in distributed power systems and renewable energy integration for over ten years and has published widely in this field. He is a registered Professional Engineer (PE) in the state of Oregon and a Certified Energy Manager (CEM) and Renewable Energy Professional (REP). Dr. Hossain received his Ph.D. in 2016 from the College of Engineering and Applied Science at the University of Wisconsin Milwaukee (UWM). He received his MS in Mechatronics and Robotics Engineering from the International Islamic University Malaysia, Malaysia, in 2010 and a BS in Electrical and Electronic Engineering from Khulna University of Engineering and Technology, Bangladesh, in 2006. His research interests include the modeling, analysis, design, and control of power electronic devices, energy storage systems, renewable energy sources, the integration of distributed generation systems, microgrid and smart grid applications, robotics, and advanced control systems. Previously, he was involved with several research projects on renewable energy and grid-tied microgrid systems at the Oregon Institute of Technology, as an Associate Professor in the Department of Electrical Engineering and Renewable Energy, and as a Senior Electrical Consultant at RRC Power and Energy. He is a senior member of the Association of Energy Engineers (AEE) and the IEEE. Dr. Hossain is the author of the books Excel Crash Course for Engineers and MATLAB and Simulink Crash Course for Engineers, co-author of Renewable Energy Crash Course: A Concise Introduction and Photovoltaic Systems: Fundamentals and Applications, and is working on several other book projects. With his dedicated research team, Dr. Hossain is exploring methods to make electric power systems more sustainable, cost-effective, and secure through extensive research and analysis onenergy storage, microgrid systems, and renewable energy sources.

Klappentext

Machine Learning Crash Course for Engineers is a reader-friendly introductory guide to machine learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement machine learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of machine learning quickly.

Inhalt
Introduction to Machine Learning.- Evaluation Criteria and Model Selection.- Machine Learning Algorithms.- Applications of Machine Learning: Signal/Image Processing.- Applications of Machine Learning: Energy Systems.- Applications of Machine Learning: Robotics.- State of the Art of Machine Learning.