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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

Product type Paperback

Published in Feb 2024

Publisher Packt

ISBN-13 9781804618127

Length 378 pages

Edition 1st Edition

Languages

Python

Concepts

Deep Learning

Authors (3):

Jonas Christensen

Manmohan Gosada

Nakul Bajaj

View More author details

Table of Contents (17) Chapters

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

Why subscribe?

16. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

active learning 45, 169

approaches 170

diversity sampling 180, 181

example 169, 170

query by committee (QBC) 177-179

uncertainty sampling 170-177

Adaptive Boosting (AdaBoost) 326

Adaptive Synthetic Sampling (ADASYN) 235-238

Amazon Mechanical Turk (AMT) 57

analysis of variance (ANOVA) analysis 71

anomaly detection 303

anomaly detection algorithms

semi-supervised methods 304, 307

supervised methods 304, 310

unsupervised methods 303, 304

anonymization 221

applications, pattern matching

fraud detection 149

image recognition 149

text classification 149

time series analysis 149

Area Under the ROC Curve (AUC-ROC) 319

artificial intelligence (AI) 53

history 20-25

autoencoders 293, 307

implementing 308-310

automated machine...

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Authors (3)

Jonas Christensen

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.

See other products by Jonas Christensen

Manmohan Gosada

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.

See other products by Manmohan Gosada

Nakul Bajaj

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.

See other products by Nakul Bajaj

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