Preface

In today’s data-driven era, where more than 2.5 quintillion bytes of data are produced daily in various forms such as text, image, audio, and video, data stands as the cornerstone of the AI revolution. However, the majority of real-world data available for training supervised machine learning models lacks labels, or we encounter limited labeled data. This presents a significant challenge, as labeled data is essential for training any supervised machine learning model and fine-tuning large language models in the age of generative AI.

To address the scarcity of labeled data and facilitate the preparation of labeled data for training supervised machine learning models and fine-tuning large language models, this book introduces various methods for programmatic data labeling using Python libraries and methods, including semi-supervised and unsupervised learning.

This book guides you through the process of loading and analyzing tabular data, images, videos, audio, and text using various Python libraries, the OpenAI API, LangChain, and Azure Machine Learning. It explores techniques such as weak supervision, pseudo-labeling, and K-means clustering for classification and labeling, while also providing data augmentation methods to enhance accuracy. Utilizing the Azure OpenAI API and LangChain, the book demonstrates the automation of data analysis using natural language without the need to acquire any programming skills. It also encompasses the classification and data labeling of text data using OpenAI and large language models (LLMs). This book covers a wide variety of open source data annotation tools, along with Azure Machine Learning, and compares the pros and cons of these tools.

Real-world examples from various industries are incorporated to illustrate the application of these methods to tabular, text, image, video, and audio data.

By the conclusion of this book, you will have acquired the skills to explore different types of data using Python and OpenAI LLMs. You will have learned how to prepare data with labels, whether for training machine learning models or unlocking insights about the data to leverage for business use cases across industries.

Who this book is for

This book is for aspiring AI engineers, machine learning engineers, data scientists, and data engineers who want to learn about data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn about data exploration and annotation using Python libraries.

What this book covers

Chapter 1, Exploring Data for Machine Learning, provides an overview of data analysis and visualization methods using various Python libraries. Additionally, it deep dives into unlocking data insights with natural language using OpenAI LLMs.

Chapter 2, Labeling Data for Classification, covers the process of labeling tabular data for training classification models. Various methods, such as Snorkel Python functions, semi-supervised learning, and clustering data using K-means, are explored.

Chapter 3, Labeling Data for Regression, addresses the labeling of tabular data for training regression models. Techniques include leveraging summary statistics, creating pseudo labels, employing data augmentation methods, and utilizing K-means clustering.

Chapter 4, Exploring Image Data, covers the analysis and visualization of image data and feature extraction from images using various Python libraries.

Chapter 5, Labeling Image Data Using Rules, discusses labeling images based on heuristics and image properties such as aspect ratio, and also covers image classification using pre-trained classifiers such as YOLO.

Chapter 6, Labeling Image Data Using Data Augmentation, explores methods of image data augmentation for training support vector machines and Convolutional Neural Networks (CNNs), as well as addressing image data labeling.

Chapter 7, Labeling Text Data, covers generative AI and various methods for labeling text data. This includes Azure OpenAI with real-world use cases, text classification, and sentiment analysis using Snorkel and K-means clustering.

Chapter 8, Exploring Video Data, focuses on loading video data, extracting features, visualizing video data, and clustering video data using K-means clustering.

Chapter 9, Labeling Video Data, delves into labeling video data using CNNs, segmenting video data with the watershed algorithm, and capturing important features using autoencoders, accompanied by real-world examples.

Chapter 10, Exploring Audio Data, provides the fundamentals of audio data, loading and visualizing audio data, extracting features, and real-life applications.

Chapter 11, Labeling Audio Data, covers transcribing audio data using OpenAI’s Whisper model, labeling the transcription, creating spectrograms for audio data classification, augmenting audio data, and using Azure Cognitive Services for speech.

Chapter 12, Hands-On Exploring Data Labeling Tools, covers various data labeling tools, including open source tools such as Label Studio, CVAT, pyOpenAnnotate, and Azure Machine Learning. It also includes a comparison of various data labeling tools for image, text, audio, and video data.

To get the most out of this book

Basic Python knowledge is beneficial but not necessary to get the most out of this book.

Software/hardware covered in the book	Operating system requirements
Python 3.9+	Windows, macOS, or Linux
Azure OpenAI subscription
ECMAScript 11

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Data-Labeling-in-Machine-Learning-with-Python. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Now let us generate the augmented data by calling the noise, scale, and rotation augmentation functions, as follows.”

A block of code is set as follows:

# Train a linear regression model on the labeled data
regressor = LinearRegression()
regressor.fit(train_data, train_labels)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

news_headline="Label the following news headline into 1 of the following categories: Business, Tech, Politics, Sport, Entertainment\n\n Headline 1: Trump is ready to contest in nov 2024 elections\nCategory:",
response = openai.Completion.create(
engine=model_deployment_name,
prompt= news_headline,
temperature=0,

Any command-line input or output is written as follows:

pip install keras

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Change System preferences | Security and privacy | General, and then select Open anyway.”

Tips or important notes

Appear like this.

Get in touch

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