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    Data Labeling in Machine Learning with Python: Explore modern ways to prepare labeled data for training and fine-tuning ML and

    Posted By: naag
    Data Labeling in Machine Learning with Python: Explore modern ways to prepare labeled data for training and fine-tuning ML and

    Data Labeling in Machine Learning with Python: Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models
    English | January 31, 2024 | ISBN: 1804610542 | 398 pages | EPUB (True) | 14.48 MB

    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 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 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