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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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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

Summary

Throughout this book, you’ve gained invaluable skills, and you now have the knowledge to reach the next frontier of ML, following a data-centric approach. However, this knowledge is only useful if you apply it, and that takes effort.

It requires you to use the tools and techniques outlined in this book. It also requires you to step outside your comfort zone and take the lead on data quality, cross-functional collaboration, and data ethics. You can’t change the world alone, so hand over this book to a colleague and get them onboard with data centricity.

As we conclude, remember that embarking on this data-centric ML journey is not a destination but a continuous process. The landscape of data and AI is ever-evolving, and so must we evolve. Let’s continue to learn, innovate, and shape the future of data science and ML together. Your data-centric journey has only just begun.

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