Data Scaling and Normalization
Chuck Sherman
"Data Scaling and Normalization: A Comprehensive Guide to Enhancing Data Quality and Model Performance" offers an in-depth exploration of the crucial techniques used in preprocessing data to enhance the quality of machine learning models. Designed for data scientists, machine learning engineers, and analysts, this book provides a clear and comprehensive understanding of how scaling and normalization techniques impact model accuracy, efficiency, and interpretability.
Inside, you'll find detailed explanations of the core concepts of data scaling and normalization, including Min-Max scaling, Z-score normalization, robust scaling, and more. The book covers when and how to apply these techniques to various types of data, from numerical to categorical, and explores their role in improving model convergence, reducing biases, and enhancing generalization.
Key topics include:
With step-by-step guidance, code examples, and expert insights, this book is an essential resource for anyone looking to master data preprocessing and take their machine learning models to the next level.
This comprehensive guide will help you understand the nuances of scaling and normalization and how they directly affect the success of your machine learning projects.
Duration - 3h 24m.
Author - Chuck Sherman.
Narrator - Ray Collins.
Published Date - Saturday, 27 January 2024.
Copyright - © 2024 Chuck Sherman ©.
Location:
United States
Description:
"Data Scaling and Normalization: A Comprehensive Guide to Enhancing Data Quality and Model Performance" offers an in-depth exploration of the crucial techniques used in preprocessing data to enhance the quality of machine learning models. Designed for data scientists, machine learning engineers, and analysts, this book provides a clear and comprehensive understanding of how scaling and normalization techniques impact model accuracy, efficiency, and interpretability. Inside, you'll find detailed explanations of the core concepts of data scaling and normalization, including Min-Max scaling, Z-score normalization, robust scaling, and more. The book covers when and how to apply these techniques to various types of data, from numerical to categorical, and explores their role in improving model convergence, reducing biases, and enhancing generalization. Key topics include: With step-by-step guidance, code examples, and expert insights, this book is an essential resource for anyone looking to master data preprocessing and take their machine learning models to the next level. This comprehensive guide will help you understand the nuances of scaling and normalization and how they directly affect the success of your machine learning projects. Duration - 3h 24m. Author - Chuck Sherman. Narrator - Ray Collins. Published Date - Saturday, 27 January 2024. Copyright - © 2024 Chuck Sherman ©.
Language:
English
Opening Credits
Duration:00:00:14
Introduction
Duration:00:02:18
Foundations of data scaling and
Duration:00:25:24
The impact on model performance
Duration:00:33:54
Methods of scaling data
Duration:00:13:58
Normalization techniques
Duration:00:12:03
Challenges and pitfalls in data scaling
Duration:00:11:56
Advanced techniques in data
Duration:00:11:02
Implementing data scaling and
Duration:00:14:26
Best practices and tips for data
Duration:00:10:05
Future trends in data scaling and
Duration:00:53:27
Case studies
Duration:00:09:45
Conclusion
Duration:00:06:07
Ending Credits
Duration:00:00:15