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Data-Centric Machine Learning with Python

You're reading from   Data-Centric Machine Learning with Python The ultimate guide to engineering and deploying high-quality models based on good data

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781804618127
Length 378 pages
Edition 1st Edition
Languages
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Authors (3):
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Jonas Christensen Jonas Christensen
Author Profile Icon Jonas Christensen
Jonas Christensen
Manmohan Gosada Manmohan Gosada
Author Profile Icon Manmohan Gosada
Manmohan Gosada
Nakul Bajaj Nakul Bajaj
Author Profile Icon Nakul Bajaj
Nakul Bajaj
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: What Data-Centric Machine Learning Is and Why We Need It FREE CHAPTER
2. Chapter 1: Exploring Data-Centric Machine Learning 3. Chapter 2: From Model-Centric to Data-Centric – ML’s Evolution 4. Part 2: The Building Blocks of Data-Centric ML
5. Chapter 3: Principles of Data-Centric ML 6. Chapter 4: Data Labeling Is a Collaborative Process 7. Part 3: Technical Approaches to Better Data
8. Chapter 5: Techniques for Data Cleaning 9. Chapter 6: Techniques for Programmatic Labeling in Machine Learning 10. Chapter 7: Using Synthetic Data in Data-Centric Machine Learning 11. Chapter 8: Techniques for Identifying and Removing Bias 12. Chapter 9: Dealing with Edge Cases and Rare Events in Machine Learning 13. Part 4: Getting Started with Data-Centric ML
14. Chapter 10: Kick-Starting Your Journey in Data-Centric Machine Learning 15. Index 16. Other Books You May Enjoy

To get the most out of this book

To extract the maximum value from this book, prior exposure to machine learning concepts, foundational knowledge of statistical methods, and familiarity with Python programming will be highly beneficial. The book is tailored for those with familiarity with the machine learning process and a desire to delve deeper into the world of data-centric machine learning and artificial intelligence.

Software/hardware covered in the book

Operating system requirements

Python 3

Windows, macOS, or Linux

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

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