Machine Learning Crash Course for Engineers
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Beschreibung
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.
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
Autor*in
Eklas Hossain
Themen in »Machine Learning Crash Course for Engineers«
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