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Key benefits

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

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.

Who is this book 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.

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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Colour book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

€29.99

Data Labeling in Machine Learning with Python

€37.99

€29.99

Total € 97.97

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Subhasish Ghosh Mar 04, 2024

Completed reading "𝐃𝐚𝐭𝐚 𝐋𝐚𝐛𝐞𝐥𝐢𝐧𝐠 𝐢𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧" authored by my colleague, Vijaya K at Microsoft. Thank you, Vijay, for authoring an excellent book, which I feel is the only detailed & most up-to-date book currently available in the market that covers OpenAI LLMs as well from the POV of Data Labeling using Python.If you're an aspirational / professional AI Engineer, Data Scientist, or Data Architect looking to transition into a full-time AI Product role, a mandatory foundational knowledge in Data Labeling in ML is a must. Because out of the 2.5 quintillion bytes of data that is generated daily, a very small number of it is useful for training LLMs, because we need "labeled" data for essentially training any supervised ML model, and fine-tuning LLMs in GenAI.5 reasons why this book is excellent:1) Hands-on examples and guides you through the process of loading & analyzing tab data, images, videos, audio etc. Provides an in-depth coverage of different Python libraries at your disposal; loved the in-depth architecture diagrams as well.2) Provides a solid explanation of weak supervision, pseudo-labeling, and K-means clustering.3) The 'Labeling Text Data' section (and chapters) is my favorite section. Provide a solid in-depth coverage of real-world apps, tools and Snorkel API. A good coverage of OpenAI GPT models with 5 use-cases for text data labeling using Completions (v1) API using GPT-3.5-turbo.// 𝘈𝘮 𝘴𝘶𝘳𝘦 𝘵𝘩𝘦 𝘤𝘰𝘥𝘦 𝘴𝘢𝘮𝘱𝘭𝘦𝘴 𝘸𝘪𝘭𝘭 𝘣𝘦 𝘶𝘱𝘥𝘢𝘵𝘦𝘥 𝘪𝘯 𝘧𝘶𝘵𝘶𝘳𝘦 𝘷𝘦𝘳𝘴𝘪𝘰𝘯𝘴 𝘰𝘧 𝘵𝘩𝘦 𝘣𝘰𝘰𝘬 𝘸𝘪𝘵𝘩 𝘎𝘗𝘛-4 𝘊𝘩𝘢𝘵𝘊𝘰𝘮𝘱𝘭𝘦𝘵𝘪𝘰𝘯𝘴 𝘷2𝘈𝘗𝘐.4) A great many real-life examples (use-cases) from different industries have been included which shows how you could use different data labeling techniques related to audio, video, text, tab and images.5) Finally, there's a full chapter on exploring different Data Labeling Tools. This covers various data labeling tools, including OSS tools such as Label Studio, CVAT, pyOpenAnnotate, and Azure ML. Excellent comparison of different tools and their inherent capabilities.In summary, this is an excellent book if you are someone looking to build a solid foundational knowledge on Data Science including ML. Basic Python knowledge is required, and you should have access to Python 3.9+, an Azure OpenAI subscription, for getting most out of this book.

Amazon Verified review

H2N Apr 02, 2024

This book is a beginner-friendly level for data labeling in AI. In this book, Python and OpenAI for labeling diverse datasets were mentioned even with minimal programming know-how. It is good for newcomers to tackle the industry's data with detailed step by step real-world applications. A perfect starting point for beginners to become proficient in preparing data for impactful machine learning projects.

Hareesh Thippaih Apr 21, 2024

Data Labeling in Machine Learning with Python is a must-have resource for anyone diving into the intricacies of data annotation, NLP and LLM models. The book brilliantly navigates through various techniques, from basic summary statistics to advanced semi-supervised learning methods, NLP and LLM models. What sets this book apart is its practical approach, providing clear explanations alongside hands-on examples using ML, DL, NLP and GenaAI Python libraries . The chapters on text and image annotation are particularly insightful, offering valuable techniques for labeling diverse datasets. Overall, this book is an indispensable companion for data scientists, ML practitioners and GenAI Engineers, equipping them with the skills to unleash the full potential of their data. Highly recommended!Excellent book

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

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