Data Labeling in Machine Learning with Python
Packt Publishing Limited (Verlag)
978-1-80461-054-1 (ISBN)
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 DescriptionData 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 forThis 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.
Vijaya Kumar Suda is a seasoned data and AI professional boasting over two decades of expertise collaborating with global clients. Having resided and worked in diverse locations such as Switzerland, Belgium, Mexico, Bahrain, India, Canada, and the USA, Vijaya has successfully assisted customers spanning various industries. Currently serving as a senior data and AI consultant at Microsoft, he is instrumental in guiding industry partners through their digital transformation endeavors using cutting-edge cloud technologies and AI capabilities. His proficiency encompasses architecture, data engineering, machine learning, generative AI, and cloud solutions.
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
| Erscheinungsdatum | 20.10.2023 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Software Entwicklung ► User Interfaces (HCI) | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-80461-054-2 / 1804610542 |
| ISBN-13 | 978-1-80461-054-1 / 9781804610541 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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