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Data Labeling in Machine Learning with Python - by Vijaya Kumar Suda (Paperback)

Data Labeling in Machine Learning with Python - by  Vijaya Kumar Suda (Paperback) - 1 of 1
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About this item

Highlights

  • Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labelingKey FeaturesGenerate labels for regression in scenarios with limited training dataApply generative AI and large language models (LLMs) to explore and label text dataLeverage Python libraries for image, video, and audio data analysis and data labelingPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine learning.
  • Author(s): Vijaya Kumar Suda
  • 398 Pages
  • Computers + Internet, Data Modeling & Design

Description



About the Book



Discover data labeling methods through Python libraries, ML algorithms, and generative AI with this guide covering best practices, advanced methods, and tools.



Book Synopsis



Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labeling

Key Features
  • Generate labels for regression in scenarios with limited training data
  • Apply generative AI and large language models (LLMs) to explore and label text data
  • Leverage Python libraries for image, video, and audio data analysis and data labeling
  • Purchase of the print or Kindle book includes a free PDF eBook
Book Description

Data labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today's data-driven world, mastering data labeling is not just an advantage, it's a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution.

With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively.

By the end of this book, you'll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.

What you will learn
  • Excel in exploratory data analysis (EDA) for tabular, text, audio, video, and image data
  • Understand how to use Python libraries to apply rules to label raw data
  • Discover data augmentation techniques for adding classification labels
  • Leverage K-means clustering to classify unsupervised data
  • Explore how hybrid supervised learning is applied to add labels for classification
  • Master text data classification with generative AI
  • Detect objects and classify images with OpenCV and YOLO
  • Uncover a range of techniques and resources for data annotation
Who this book is for

This book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.

Table of Contents
  1. Exploring Data for Machine Learning
  2. Labeling Data for Classification
  3. Labeling Data for Regression
  4. Exploring Image Data
  5. Labeling Image Data Using Rules
  6. Labeling Image Data Using Data Augmentation
  7. Labeling Text Data
  8. Exploring Video Data
  9. Labeling Video Data
  10. Exploring Audio Data
  11. Labeling Audio Data
  12. Hands-On Exploring Data Labeling Tools
Dimensions (Overall): 9.25 Inches (H) x 7.5 Inches (W) x .82 Inches (D)
Weight: 1.5 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 398
Genre: Computers + Internet
Sub-Genre: Data Modeling & Design
Publisher: Packt Publishing
Format: Paperback
Author: Vijaya Kumar Suda
Language: English
Street Date: January 31, 2024
TCIN: 1004983913
UPC: 9781804610541
Item Number (DPCI): 247-14-7558
Origin: Made in the USA or Imported
If the item details above aren’t accurate or complete, we want to know about it.

Shipping details

Estimated ship dimensions: 0.82 inches length x 7.5 inches width x 9.25 inches height
Estimated ship weight: 1.5 pounds
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