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

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.

將您的資料準備、機器學習和GenAI技能提升到更高水平，通過學習一系列Python演算法和數據標記工具。

主要特點：
- 在訓練數據有限的情況下，生成回歸標籤。
- 應用生成式AI和大型語言模型（LLMs）來探索和標記文本數據。
- 利用Python庫進行圖像、視頻和音頻數據分析和標記。
- 購買印刷版或Kindle電子書，包括免費的PDF電子書。

書籍描述：
數據標記是引導人工智能和機器學習強大力量的無形之手。在當今數據驅動的世界中，掌握數據標記不僅是一種優勢，而且是一種必需。《Python機器學習中的數據標記》使您能夠從原始數據中發現價值，創建智能系統，並影響技術演進的方向。

通過本書，您將學習如何使用摘要統計、弱監督、程序規則和啟發式方法，以編程方式為未標記的訓練數據分配標籤。隨著學習的進展，您將能夠通過掌握半監督學習和數據擴增的細節來增強數據集。在深入探索數據領域的過程中，您將深入研究圖像、視頻和音頻數據的標註，利用Python庫（如seaborn、matplotlib、cv2、librosa、openai和langchain）的強大功能。通過實際指導和實例，您將熟練地標註各種數據類型。

通過閱讀本書，您將具備以編程方式標註各種數據類型並增強數據集的實際專業知識，發揮數據的全部潛力。

您將學到：
- 在表格、文本、音頻、視頻和圖像數據的探索性數據分析（EDA）方面優秀。
- 理解如何使用Python庫對原始數據應用規則進行標註。
- 發現添加分類標籤的數據擴增技術。
- 利用K-means聚類對非監督數據進行分類。
- 探索如何應用混合監督學習為分類添加標籤。
- 通過生成式AI進行文本數據分類。
- 使用OpenCV和YOLO檢測對象並分類圖像。
- 揭示一系列數據標註技術和資源。

本書適合機器學習工程師、數據科學家和數據工程師，他們想要學習模型訓練的數據標註方法和演算法。數據愛好者和Python開發人員可以使用本書學習使用Python庫進行數據探索和標註。基本的Python知識對於入門並不是必需的。