​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.

Table of Contents

1. Introduction to Machine Learning

2. Evaluation Criteria and Model Selection

3. Machine Learning Algorithms

4. Applications of Machine Learning: Signal Processing

5. Applications of Machine Learning: Energy Systems

6. Applications of Machine Learning: Robotics

7. State of the Art of Machine Learning

About the Author

Dr. Eklas Hossain is an associate professor in the Department of Electrical Engineering and Renewable Energy and an Associate Researcher with the Oregon Renewable Energy Center (OREC) at the Oregon Institute of Technology (OIT), which is home to the only ABET-accredited BS and MS programs in renewable energy. He has been working in the area of distributed power systems and renewable energy integration for last ten years and has published a number of research papers and posters in this field. He is currently involved with several research projects on renewable energy and grid-tied microgrid systems at OIT. He received his PhD from the College of Engineering and Applied Science at the University of Wisconsin Milwaukee (UWM), his MS in Mechatronics and Robotics Engineering from International Islamic University of Malaysia, and a BS in Electrical & Electronic Engineering from Khulna University of Engineering and Technology, Bangladesh. Dr. Hossain is a registered Professional Engineer (PE) in the state of Oregon and is also a Certified Energy Manager (CEM) and Renewable Energy Professional (REP). He is a senior member of the Association of Energy Engineers (AEE) and an Associate Editor for IEEE Access, IEEE Systems Journal, and IET Renewable Power Generation. His research interests include the modelling, analysis, design, and control of power electronic devices; energy storage systems; renewable energy sources; integration of distributed generation systems; carbon sequestration; microgrid and smart grid applications; robotics, and advanced control