Data Labeling in Machine Learning with Python : Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models
Language: English
Published by Packt Publishing, 2024
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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nach der Bestellung gedruckt Neuware - Printed after ordering - 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 Elektronisches BuchBook 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 learnExcel in exploratory data analysis (EDA) for tabular, text, audio, video, and image dataUnderstand how to use Python libraries to apply rules to label raw dataDiscover data augmentation techniques for adding classification labelsLeverage K-means clustering to classify unsupervised dataExplore how hybrid supervised learning is applied to add labels for classificationMaster text data classification with generative AIDetect objects and classify images with OpenCV and YOLOUncover a range of techniques and resources for data annotationWho 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.Table of ContentsExploring Data for Machine LearningLabeling Data for ClassificationLabeling Data for RegressionExploring Image DataLabeling Image Data Using RulesLabeling Image Data Using Data AugmentationLabeling Text DataExploring Video DataLabeling Video DataExploring Audio DataLabeling Audio DataHands-On Exploring Data Labeling Tools.…
Seller Inventory # 9781804610541
- Title
- Data Labeling in Machine Learning with Python : Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models
- Author
- Vijaya Kumar Suda
- Publisher
- Packt Publishing
- Publication year
- 2024
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1804610542
- ISBN 13
- 9781804610541
- Item weight
- 741 grams
- Dimensions
- 235x191x21 mm
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 LLMs (large language models) 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, and librosa. 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
- Labelling the Text Data
- Exploring Video Data
- Labeling Video Data
- Exploring Audio Data
- Labeling Audio Data
- Hands-On Exploring Data Labeling Tools
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
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