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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Design of Experiments (DOE) has long been a cornerstone of innovation, enabling industries and researchers to optimize processes, improve product quality, and make data-driven decisions. From engineering and manufacturing to pharmaceuticals and materials science, DOE's application has been transformative. Yet, with artificial intelligence and data-driven tools, DOE is experiencing a revolution, opening new doors for adaptive experimentation and optimization.
In Mastering Design of Experiments: Principles, Applications, and Advanced Techniques with Python: Classic Methods, Modern Applications, and Machine Learning Integration is a comprehensive guide for professionals, students, and researchers seeking to bridge the gap between classical DOE techniques and modern AI-driven methodologies. This book is designed to provide readers with the foundational principles of DOE while introducing cutting-edge computational methods using Python, making it an indispensable resource for mastering experimentation in the 21st century.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Design of Experiments (DOE) has long been a cornerstone of innovation, enabling industries and researchers to optimize processes, improve product quality, and make data-driven decisions. From engineering and manufacturing to pharmaceuticals and materials science, DOE's application has been transformative. Yet, with artificial intelligence and data-driven tools, DOE is experiencing a revolution, opening new doors for adaptive experimentation and optimization.
In Mastering Design of Experiments: Principles, Applications, and Advanced Techniques with Python: Classic Methods, Modern Applications, and Machine Learning Integration is a comprehensive guide for professionals, students, and researchers seeking to bridge the gap between classical DOE techniques and modern AI-driven methodologies. This book is designed to provide readers with the foundational principles of DOE while introducing cutting-edge computational methods using Python, making it an indispensable resource for mastering experimentation in the 21st century.