Overview
- 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
Buy print copy
About this book
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.
Similar content being viewed by others
Keywords
- Application of machine learning
- Supervised learning
- Unsupervised learning
- Machine learning
- Signal processing
- Machine learning algorithms
- Deep learning
- Computational intelligence
- Neural networks
- Semi-supervised learning
- Reinforcement learning
- Machine learning textbook
- Introduction to machine learning
Table of contents (7 chapters)
-
Front Matter
-
Back Matter
Authors and Affiliations
About the author
Eklas Hossain, Ph.D., is an Associate Professor in the Department of Electrical and Computer Engineering at Boise State University, Idaho, USA, and a registered Professional Engineer (PE) in Oregon, USA. He received his Ph.D. from the College of Engineering and Applied Science at the University of Wisconsin Milwaukee (UWM), his MS in Mechatronics and Robotics Engineering from the International Islamic University Malaysia, and a BS in Electrical and Electronic Engineering from Khulna University of Engineering and Technology, Bangladesh, in 2016, 2010, and 2006 respectively. As the director of the iPower research laboratory, Dr. Hossain has been actively working in electrical power systems and power electronics and has published many research papers and posters. In addition, he has served as an Associate Editor for multiple international journals and is the author of several books, including MATLAB and Simulink Crash Course for Engineers (Springer, 2022). He has been an IEEE Member since 2009 and an IEEE Senior Member since 2017. His research interests include power system studies, encompassing the utility grid, microgrid, smart grid, renewable energy, energy storage systems, and power electronics, which span various converter and inverter topologies and control systems. The author has worked on several research projects on machine learning, big data, and deep learning applications in power systems, including load forecasting, renewable energy systems, and smart grids. With his dedicated research team and a group of Ph.D. students, Dr. Hossain looks forward to exploring methods to make electric power systems more sustainable, cost-effective, and secure through extensive research and analysis on grid resilience, renewable energy systems, second-life batteries, marine and hydrokinetic systems, and machine learning applications in renewable energy systems, power electronics, and climate change mitigation.
Bibliographic Information
Book Title: Machine Learning Crash Course for Engineers
Authors: Eklas Hossain
DOI: https://doi.org/10.1007/978-3-031-46990-9
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Hardcover ISBN: 978-3-031-46989-3Published: 03 January 2024
Softcover ISBN: 978-3-031-46992-3Due: 16 January 2025
eBook ISBN: 978-3-031-46990-9Published: 26 December 2023
Edition Number: 1
Number of Pages: XX, 453
Number of Illustrations: 14 b/w illustrations, 156 illustrations in colour
Topics: Machine Learning, Computational Intelligence, Electrical Engineering, Signal, Image and Speech Processing, Energy Systems