Textbook in PDF format

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
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:
Exploring Data for Machine Learning
Labeling Data for Classification
Labeling Data for Regression
Exploring Image Data
Labeling Image Data Using Rules
Labeling Image Data Using Data Augmentation
Labeling Text Data
Exploring Video Data
Labeling Video Data
Exploring Audio Data
Labeling Audio Data
Hands-On Exploring Data Labeling Tools