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

by: Vijaya Kumar Suda (Author)

Publisher: Packt Publishing – ebooks Account

Publication Date: 9 Jan. 2024

Language: English

Print Length: 360 pages

ISBN-10: 1804610542

ISBN-13: 9781804610541

Book Description

Take your data preparation, machine leaing, and GenAI skills to the next level by leaing a range of Python algorithms and tools for data labelingKey FeaturesGenerate labels for regression in scenarios with limited training dataApply Generative AI and LLMs (large language models) 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 eBookBook DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine leaing. In today’s data-driven world, mastering data labeling is not just an advantage, it’s a necessity. Data Labeling in Machine Leaing 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 leaing and data augmentation. Venturing further into the data landscape, you’ll immerse yourself in the annotation of image, video, and audio data, haessing the power of Python libraries such as seabo, 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 leaExcel 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 leaing 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 leaing engineers, data scientists, and data engineers who want to lea data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to lea data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.Table of ContentsExploring Data for Machine LeaingLabeling Data for ClassificationLabeling Data for RegressionExploring Image DataLabeling Image Data Using RulesLabeling Image Data Using Data Augmentation Labelling the Text DataExploring Video DataLabeling Video DataExploring Audio DataLabeling Audio DataHands-On Exploring Data Labeling Tools
About the Author
Take your data preparation, machine leaing, and GenAI skills to the next level by leaing a range of Python algorithms and tools for data labelingKey FeaturesGenerate labels for regression in scenarios with limited training dataApply Generative AI and LLMs (large language models) 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 eBookBook DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine leaing. In today’s data-driven world, mastering data labeling is not just an advantage, it’s a necessity. Data Labeling in Machine Leaing 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 leaing and data augmentation. Venturing further into the data landscape, you’ll immerse yourself in the annotation of image, video, and audio data, haessing the power of Python libraries such as seabo, 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 leaExcel 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 leaing 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 leaing engineers, data scientists, and data engineers who want to lea data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to lea data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.Table of ContentsExploring Data for Machine LeaingLabeling Data for ClassificationLabeling Data for RegressionExploring Image DataLabeling Image Data Using RulesLabeling Image Data Using Data Augmentation Labelling the Text DataExploring Video DataLabeling Video DataExploring Audio DataLabeling Audio DataHands-On Exploring Data Labeling Tools