Hossain, Eklas. creator author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access XX, 453 p. 170 illus., 156 illus. in color. online resource. 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. 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. by Eklas Hossain. Machine learning Computational intelligence Electrical engineering Signal processing Electric power production Machine Learning Computational Intelligence Electrical and Electronic Engineering Signal, Speech and Image Processing Electrical Power Engineering Q325.5-.7 9783031469909 https://doi.org/10.1007/978-3-031-46990-9 https://doi.org/10.1007/978-3-031-46990-9 231226 20251030110859.0 978-3-031-46990-9