Machine Learning Crash Course for Engineers

《機器學習工程師速成課程》是一本讀者友善的機器學習算法和技術入門指南，針對學生、工程師和其他忙碌的技術專業人士。本書著重於機器學習的應用方面，從基礎到高級主題，系統地從理論到應用和Python編程實例進行介紹。書中提供了高度圖解的逐步演示，讓讀者能夠實施機器學習模型來解決現實世界的問題。這本強大的教程是那些需要快速建立扎實機器學習基礎理解的人的優秀資源。

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.

Eklas Hossain博士是美國愛達荷州波伊西州立大學電機與電腦工程系的副教授，也是美國俄勒岡州的註冊專業工程師（PE）。他於2016年、2010年和2006年分別在威斯康辛密爾沃基大學工程與應用科學學院、馬來西亞國際伊斯蘭大學機電一體化與機器人工程學碩士學位和孟加拉國Khulna工程與技術大學獲得博士學位。作為iPower研究實驗室的主任，Hossain博士一直在電力系統和功率電子領域積極從事研究工作，並發表了許多研究論文和海報。此外，他曾擔任多個國際期刊的副編輯，並撰寫了幾本書，包括《MATLAB和Simulink工程師速成課程》（Springer，2022年）。他自2009年起成為IEEE會員，並於2017年成為IEEE高級會員。他的研究興趣包括電力系統研究，包括公用事業電網、微電網、智能電網、可再生能源、儲能系統和功率電子，涵蓋各種變換器和逆變器拓撲和控制系統。該作者在機器學習、大數據和深度學習在電力系統中的應用方面進行了多項研究項目，包括負載預測、可再生能源系統和智能電網。Hossain博士希望通過對電網恢復力、可再生能源系統、二次使用電池、海洋和水動力系統以及機器學習在可再生能源系統、功率電子和氣候變化減緩中的應用進行廣泛的研究和分析，探索使電力系統更可持續、具有成本效益和安全的方法。