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Data-Centric Machine Learning with Python

By Jonas Christensen , Nakul Bajaj , Manmohan Gosada
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50+ new titles added every month on new and emerging tech
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50+ new titles added every month on new and emerging tech
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This book & 6500+ ebooks & video courses on 1000+ technologies
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50+ new titles added every month on new and emerging tech
Early Access to eBooks as they are being written
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Customised display settings for better reading experience
50+ new titles added every month on new and emerging tech
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Download this book in EPUB and PDF formats
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  1. Free Chapter
    Part 1: What Data-Centric Machine Learning Is and Why We Need It
About this book
In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.
Publication date:
February 2024
Publisher
Packt
Pages
378
ISBN
9781804618127

 

Part 1: What Data-Centric Machine Learning Is and Why We Need It

In this part, we take a deep dive into data-centric machine learning, contrasting it with model-centric approaches. We use real-life examples to illustrate their differences and explore the evolution of AI and ML toward a data-centric perspective. We also dispel the myth of “big data,” highlighting the importance of quality over quantity, and the potential for democratizing ML solutions. Prepare for a fresh perspective on the transformative power of data in ML.

This part has the following chapters:

  • Chapter 1, Exploring Data-Centric Machine Learning
  • Chapter 2, From Model-Centric to Data-Centric – ML’s Evolution
About the Authors
  • Jonas Christensen

    Jonas Christensen has spent his career leading data science functions across multiple industries. He is an international keynote speaker, postgraduate educator, and advisor in the fields of data science, analytics leadership, and machine learning and host of the Leaders of Analytics podcast.

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  • Nakul Bajaj

    Nakul Bajaj is a data scientist, MLOps engineer, educator and mentor, helping students and junior engineers navigate their data journey. He has a strong passion for MLOps, with a focus on reducing complexity and delivering value from machine learning use-cases in business and healthcare.

    Browse publications by this author
  • Manmohan Gosada

    Manmohan Gosada is a seasoned professional with a proven track record in the dynamic field of data science. With a comprehensive background spanning various data science functions and industries, Manmohan has emerged as a leader in driving innovation and delivering impactful solutions. He has successfully led large-scale data science projects, leveraging cutting-edge technologies to implement transformative products. With a postgraduate degree, he is not only well-versed in the theoretical foundations of data science but is also passionate about sharing insights and knowledge. A captivating speaker, he engages audiences with a blend of expertise and enthusiasm, demystifying complex concepts in the world of data science.

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Data-Centric Machine Learning with Python
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