Modern machine learning is unimaginable without quality data. In the book "Data Labeling in Machine Learning with Python" author Vijaya Kumar Suda offers an in-depth and comprehensive guide to data annotation using the Python programming language. This manual is perfect for both beginners and experienced professionals in the field of machine learning.
You will learn how to properly label data, which tools to use, and how to avoid common mistakes. Download the book "Data Labeling in Machine Learning with Python" in PDF now to start improving your skills and enhance the quality of your machine learning projects. Data annotation is a key step in creating highly effective models, and Suda's book will be your indispensable assistant.
Who Is This Edition Recommended For?
- Developers and Machine Learning Engineers. Learn how to improve the quality of your data and, consequently, your models.
- Data Analysts. Gain skills for proper data annotation, which will increase the accuracy of your analyses.
- Students and Beginners. Discover the basic concepts and tools of data annotation, accelerating your professional growth.
- Researchers. Study advanced data annotation methods that will aid in conducting scientific research.
- Project Managers. Understand how to better manage data annotation processes in your teams to achieve better results.
What's Inside the Book "Data Labeling in Machine Learning with Python" by Vijaya Kumar Suda?
The guide "Data Labeling in Machine Learning with Python" covers all key aspects of data annotation. It starts with an introduction to the basics of data annotation, discussing fundamental concepts and methods. The book then delves into more complex techniques, explaining how to use various Python tools and libraries for data annotation.
You will learn about tools such as Labelbox, LabelImg, and VGG Image Annotator, and receive step-by-step instructions on how to use them. The book also covers advanced data annotation methods, including automation processes using machine learning and deep learning.
Additionally, special attention is given to data quality issues and methods for improvement, as well as the ethical aspects of annotation. The concluding part of the book is devoted to practical examples and real-life case studies, allowing you to apply the knowledge gained and achieve real results in your projects.
More About the Author of the Book
FAQ for "Data Labeling in Machine Learning with Python"
What advantages does the book "Data Labeling in Machine Learning with Python" offer?
The guide provides a comprehensive overview of all aspects of data annotation, from basic concepts to advanced techniques. You will gain practical skills necessary to improve data and model quality.
What Python tools are described in the manual?
It covers tools such as Labelbox, LabelImg, and VGG Image Annotator. These tools will help you efficiently annotate data for various machine learning tasks.
Is the manual suitable for beginners?
Yes, it is designed for specialists of all levels. Beginners will gain fundamental knowledge, while experienced users can deepen their skills and learn about advanced methods.
What real-life case studies are covered in the book?
The book presents several practical examples and real-life case studies that demonstrate how to apply theoretical knowledge in practice. This includes data annotation for computer vision, natural language processing, and other areas.
What ethical aspects of data annotation are discussed?
The author addresses ethical issues in data annotation, including privacy, data bias, and ways to minimize them. You will learn how to ensure fairness and accuracy in your project's data.
How does the book help improve data quality?
The author offers various methods and strategies for improving data quality, such as data validation, improving annotation processes, and using automated tools for quality control.
Information
| Author: | Vijaya Kumar Suda | Language: | English |
| Publisher: | Packt Publishing | ISBN-13: | 978-1804610541 |
| Publication Date: | January 31, 2024 | ISBN-10: | 1804610542 |
| Print Length: | 398 pages |
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