1.

Record Nr.

UNINA9910799241003321

Autore

Hossain Eklas

Titolo

Machine Learning Crash Course for Engineers [[electronic resource] /] / by Eklas Hossain

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024

ISBN

3-031-46990-9

Edizione

[1st ed. 2024.]

Descrizione fisica

1 online resource (465 pages)

Disciplina

006.31

Soggetti

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

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

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.

Sommario/riassunto

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.