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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

Technical requirements

We are going to install the following Python libraries.

openai-whisper is the Python library provided by OpenAI, offering access to the powerful Whisper Automatic Speech Recognition (ASR) model. It allows you to transcribe audio data with state-of-the-art accuracy:

%pip install openai-whisper

librosa is a Python package for music and audio analysis. It provides tools for various tasks, such as loading audio files, extracting features, and performing transformations, making it a valuable library for audio data processing:

%pip install librosa

pytube is a lightweight, dependency-free Python library for downloading YouTube videos. It simplifies the process of fetching video content from YouTube, making it suitable for various applications involving YouTube data:

%pip install pytube

transformers is a popular Python library developed by Hugging Face. It provides pre-trained models and various utilities for natural language processing (NLP) tasks...

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