Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Data Labeling in Machine Learning with Python
Data Labeling in Machine Learning with Python

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

Arrow left icon
Profile Icon Vijaya Kumar Suda
Arrow right icon
$35.98 $39.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (3 Ratings)
eBook Jan 2024 398 pages 1st Edition
eBook
$35.98 $39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Vijaya Kumar Suda
Arrow right icon
$35.98 $39.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (3 Ratings)
eBook Jan 2024 398 pages 1st Edition
eBook
$35.98 $39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$35.98 $39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $19.99p/m

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
OR
Modal Close icon
Payment Processing...
tick Completed

Billing Address

Table of content icon View table of contents Preview book icon Preview Book

Data Labeling in Machine Learning with Python

Left arrow icon Right arrow icon
Download code icon Download Code

Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 31, 2024
Length: 398 pages
Edition : 1st
Language : English
ISBN-13 : 9781804613788
Category :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
OR
Modal Close icon
Payment Processing...
tick Completed

Billing Address

Product Details

Publication date : Jan 31, 2024
Length: 398 pages
Edition : 1st
Language : English
ISBN-13 : 9781804613788
Category :

Packt Subscriptions

See our plans and pricing
Modal Close icon
$19.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
$199.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts
$279.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total $ 129.97
Principles of Data Science
$39.99
Data Labeling in Machine Learning with Python
$49.99
Mastering Transformers
$39.99
Total $ 129.97 Stars icon

Table of Contents

17 Chapters
Part 1: Labeling Tabular Data Chevron down icon Chevron up icon
Chapter 1: Exploring Data for Machine Learning Chevron down icon Chevron up icon
Chapter 2: Labeling Data for Classification Chevron down icon Chevron up icon
Chapter 3: Labeling Data for Regression Chevron down icon Chevron up icon
Part 2: Labeling Image Data Chevron down icon Chevron up icon
Chapter 4: Exploring Image Data Chevron down icon Chevron up icon
Chapter 5: Labeling Image Data Using Rules Chevron down icon Chevron up icon
Chapter 6: Labeling Image Data Using Data Augmentation Chevron down icon Chevron up icon
Part 3: Labeling Text, Audio, and Video Data Chevron down icon Chevron up icon
Chapter 7: Labeling Text Data Chevron down icon Chevron up icon
Chapter 8: Exploring Video Data Chevron down icon Chevron up icon
Chapter 9: Labeling Video Data Chevron down icon Chevron up icon
Chapter 10: Exploring Audio Data Chevron down icon Chevron up icon
Chapter 11: Labeling Audio Data Chevron down icon Chevron up icon
Chapter 12: Hands-On Exploring Data Labeling Tools Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(3 Ratings)
5 star 100%
4 star 0%
3 star 0%
2 star 0%
1 star 0%
Subhasish Ghosh Mar 04, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Completed reading "𝐃𝐚𝐭𝐚 𝐋𝐚𝐛𝐞𝐥𝐢𝐧𝐠 𝐢𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧" authored by my colleague, Vijaya K at Microsoft. Thank you, Vijay, for authoring an excellent book, which I feel is the only detailed & most up-to-date book currently available in the market that covers OpenAI LLMs as well from the POV of Data Labeling using Python.If you're an aspirational / professional AI Engineer, Data Scientist, or Data Architect looking to transition into a full-time AI Product role, a mandatory foundational knowledge in Data Labeling in ML is a must. Because out of the 2.5 quintillion bytes of data that is generated daily, a very small number of it is useful for training LLMs, because we need "labeled" data for essentially training any supervised ML model, and fine-tuning LLMs in GenAI.5 reasons why this book is excellent:1) Hands-on examples and guides you through the process of loading & analyzing tab data, images, videos, audio etc. Provides an in-depth coverage of different Python libraries at your disposal; loved the in-depth architecture diagrams as well.2) Provides a solid explanation of weak supervision, pseudo-labeling, and K-means clustering.3) The 'Labeling Text Data' section (and chapters) is my favorite section. Provide a solid in-depth coverage of real-world apps, tools and Snorkel API. A good coverage of OpenAI GPT models with 5 use-cases for text data labeling using Completions (v1) API using GPT-3.5-turbo.// 𝘈𝘮 𝘴𝘶𝘳𝘦 𝘵𝘩𝘦 𝘤𝘰𝘥𝘦 𝘴𝘢𝘮𝘱𝘭𝘦𝘴 𝘸𝘪𝘭𝘭 𝘣𝘦 𝘶𝘱𝘥𝘢𝘵𝘦𝘥 𝘪𝘯 𝘧𝘶𝘵𝘶𝘳𝘦 𝘷𝘦𝘳𝘴𝘪𝘰𝘯𝘴 𝘰𝘧 𝘵𝘩𝘦 𝘣𝘰𝘰𝘬 𝘸𝘪𝘵𝘩 𝘎𝘗𝘛-4 𝘊𝘩𝘢𝘵𝘊𝘰𝘮𝘱𝘭𝘦𝘵𝘪𝘰𝘯𝘴 𝘷2𝘈𝘗𝘐.4) A great many real-life examples (use-cases) from different industries have been included which shows how you could use different data labeling techniques related to audio, video, text, tab and images.5) Finally, there's a full chapter on exploring different Data Labeling Tools. This covers various data labeling tools, including OSS tools such as Label Studio, CVAT, pyOpenAnnotate, and Azure ML. Excellent comparison of different tools and their inherent capabilities.In summary, this is an excellent book if you are someone looking to build a solid foundational knowledge on Data Science including ML. Basic Python knowledge is required, and you should have access to Python 3.9+, an Azure OpenAI subscription, for getting most out of this book.
Amazon Verified review Amazon
H2N Apr 02, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a beginner-friendly level for data labeling in AI. In this book, Python and OpenAI for labeling diverse datasets were mentioned even with minimal programming know-how. It is good for newcomers to tackle the industry's data with detailed step by step real-world applications. A perfect starting point for beginners to become proficient in preparing data for impactful machine learning projects.
Amazon Verified review Amazon
Hareesh Thippaih Apr 21, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Data Labeling in Machine Learning with Python is a must-have resource for anyone diving into the intricacies of data annotation, NLP and LLM models. The book brilliantly navigates through various techniques, from basic summary statistics to advanced semi-supervised learning methods, NLP and LLM models. What sets this book apart is its practical approach, providing clear explanations alongside hands-on examples using ML, DL, NLP and GenaAI Python libraries . The chapters on text and image annotation are particularly insightful, offering valuable techniques for labeling diverse datasets. Overall, this book is an indispensable companion for data scientists, ML practitioners and GenAI Engineers, equipping them with the skills to unleash the full potential of their data. Highly recommended!Excellent book
Amazon Verified review Amazon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

How do I buy and download an eBook? Chevron down icon Chevron up icon

Where there is an eBook version of a title available, you can buy it from the book details for that title. Add either the standalone eBook or the eBook and print book bundle to your shopping cart. Your eBook will show in your cart as a product on its own. After completing checkout and payment in the normal way, you will receive your receipt on the screen containing a link to a personalised PDF download file. This link will remain active for 30 days. You can download backup copies of the file by logging in to your account at any time.

If you already have Adobe reader installed, then clicking on the link will download and open the PDF file directly. If you don't, then save the PDF file on your machine and download the Reader to view it.

Please Note: Packt eBooks are non-returnable and non-refundable.

Packt eBook and Licensing When you buy an eBook from Packt Publishing, completing your purchase means you accept the terms of our licence agreement. Please read the full text of the agreement. In it we have tried to balance the need for the ebook to be usable for you the reader with our needs to protect the rights of us as Publishers and of our authors. In summary, the agreement says:

  • You may make copies of your eBook for your own use onto any machine
  • You may not pass copies of the eBook on to anyone else
How can I make a purchase on your website? Chevron down icon Chevron up icon

If you want to purchase a video course, eBook or Bundle (Print+eBook) please follow below steps:

  1. Register on our website using your email address and the password.
  2. Search for the title by name or ISBN using the search option.
  3. Select the title you want to purchase.
  4. Choose the format you wish to purchase the title in; if you order the Print Book, you get a free eBook copy of the same title. 
  5. Proceed with the checkout process (payment to be made using Credit Card, Debit Cart, or PayPal)
Where can I access support around an eBook? Chevron down icon Chevron up icon
  • If you experience a problem with using or installing Adobe Reader, the contact Adobe directly.
  • To view the errata for the book, see www.packtpub.com/support and view the pages for the title you have.
  • To view your account details or to download a new copy of the book go to www.packtpub.com/account
  • To contact us directly if a problem is not resolved, use www.packtpub.com/contact-us
What eBook formats do Packt support? Chevron down icon Chevron up icon

Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks? Chevron down icon Chevron up icon
  • You can get the information you need immediately
  • You can easily take them with you on a laptop
  • You can download them an unlimited number of times
  • You can print them out
  • They are copy-paste enabled
  • They are searchable
  • There is no password protection
  • They are lower price than print
  • They save resources and space
What is an eBook? Chevron down icon Chevron up icon

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.