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Data Labeling in Machine Learning with Python

You're reading from Data Labeling in Machine Learning with Python Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models

Product type Paperback

Published in Jan 2024

Publisher Packt

ISBN-13 9781804610541

Length 398 pages

Edition 1st Edition

Languages

Python

Tools

Excel

Concepts

Machine Learning

Author (1):

Vijaya Kumar Suda

View More author details

Table of Contents (18) Chapters

Preface

1. Part 1: Labeling Tabular Data

2. Chapter 1: Exploring Data for Machine Learning FREE CHAPTER

3. Chapter 2: Labeling Data for Classification

4. Chapter 3: Labeling Data for Regression

5. Part 2: Labeling Image Data

6. Chapter 4: Exploring Image Data

7. Chapter 5: Labeling Image Data Using Rules

8. Chapter 6: Labeling Image Data Using Data Augmentation

9. Part 3: Labeling Text, Audio, and Video Data

10. Chapter 7: Labeling Text Data

11. Chapter 8: Exploring Video Data

12. Chapter 9: Labeling Video Data

13. Chapter 10: Exploring Audio Data

14. Chapter 11: Labeling Audio Data

15. Chapter 12: Hands-On Exploring Data Labeling Tools

16. Index

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17. Other Books You May Enjoy

Technical requirements

For this chapter, we will use the CIFAR-10 dataset, which is a publicly available image dataset consisting of 60,000 32x32 color images in 10 classes (http://www.cs.toronto.edu/~kriz/cifar.html), along with the famous MNIST handwritten digits dataset.

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Authors (1)

Vijaya Kumar Suda

Vijaya Kumar Suda

Vijaya Kumar Suda is a seasoned data and AI professional boasting over two decades of expertise collaborating with global clients. Having resided and worked in diverse locations such as Switzerland, Belgium, Mexico, Bahrain, India, Canada, and the USA, Vijaya has successfully assisted customers spanning various industries. Currently serving as a senior data and AI consultant at Microsoft, he is instrumental in guiding industry partners through their digital transformation endeavors using cutting-edge cloud technologies and AI capabilities. His proficiency encompasses architecture, data engineering, machine learning, generative AI, and cloud solutions.

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