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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

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781804610541
Length 398 pages
Edition 1st Edition
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Author (1):
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Vijaya Kumar Suda Vijaya Kumar Suda
Author Profile Icon Vijaya Kumar Suda
Vijaya Kumar Suda
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Table of Contents (18) Chapters Close

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 17. Other Books You May Enjoy

Exploring Video Data

In today’s data-driven world, videos have become a significant source of information and insights. Analyzing video data can provide valuable knowledge about human actions, scene understanding, and various real-world phenomena. In this chapter, we will embark on an exciting journey to explore and understand video data using the powerful combination of Python, Matplotlib, and cv2.

We will start by learning how to use the cv2 library, a popular computer vision library in Python, to read in video data. With cv2, we can effortlessly load video files, access individual frames, and perform various operations on them. These fundamental skills set the stage for our exploration and analysis.

Next, we will dive into the process of extracting frames from video data. Video frames are the individual images that make up a video sequence. Extracting frames allows us to work with individual snapshots, enabling us to analyze, manipulate, and extract useful insights...

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