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Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
Vendedor de IberLibro desde 7 de abril de 2005
Condición: Nuevo
EUR 81,54
Cantidad disponible: Más de 20 disponibles
Añadir al carritoDescripción del artículo del vendedor
New Book. Shipped from UK. Established seller since 2000.
N° de ref. del artículo L2-9798868815812
- Título
- Mastering Text Analytics
- Autor
- Shailendra Kadre
- Editorial
- Apress
- Año de publicación
- 2025
- Estado
- New
- Encuadernación
- PAP
- Idioma
- inglés
- ISBN 13
- 9798868815812
- Peso del artículo
- 503 gramos
This book is a comprehensive guide to mastering Natural Language Processing (NLP), a rapidly growing field in AI-powered text and data analytics. It equips you with tools and techniques to extract valuable insights from both structured and unstructured data, enabling you to uncover insights beyond the reach of traditional data analysis methods and stay competitive in this evolving domain.
The book starts with foundational concepts, such as collecting and extracting data for NLP projects, before progressing to advanced topics like applications of transfer learning in NLP and Large Language Models (LLMs). Each chapter emphasizes real-world applications and includes practical case studies to ensure the knowledge is immediately applicable. Throughout the book, readers will find Python code demonstrations, hands-on projects, and detailed explanations of key concepts. Special features include business use cases from industries like healthcare and customer service, practice exercises to reinforce learning, and explorations of emerging NLP technologies. These elements make the book not only informative but also highly engaging and interactive.
By the end of the book, the reader will have a solid foundation in Generative AI techniques to apply them to complex challenges. Whether you’re a budding data scientist or a seasoned professional, this guide will help you harness the power of AI-driven text and data analytics effectively.
What you will learn:
- Understand NLP with easy-to-follow explanations, examples, and Python implementations.
- Explore techniques such as transformers, word embeddings, and pragmatic analysis in real-world contexts.
- Work with real-world datasets and apply pre-processing, tokenization, and text extraction using NLP libraries.
- How to build complete NLP pipelines from data collection to model implementation, including sentiment analysis and chatbots.
- Learn state-of-the-art methods like deep learning techniques in NLP, large language models (LLMs), and zero-shot learning in NLP.
Who this book is for:
This book is tailored for data scientists, machine learning engineers, AI practitioners, and software developers seeking to learn NLP techniques and apply them to solve problems.
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Shailendra Kadre is a seasoned professional in machine learning, deep learning, product development, and project management, with 17 years of industry experience at top-notch IT products and services companies. He has held leadership positions in Machine Learning and Product Analytics at HP Inc. and Satyam. Shailendra holds a master’s degree in Design Engineering from the Indian Institute of Technology (IIT), Delhi, and an M.Sc. in ML & AI from Liverpool JM University, UK. He is also a certified Project Management Professional (PMP) from the Project Management Institute (PMI). He is the author of the book Practical Business Analytics Using SAS, published by Apress, which has received positive reviews on Amazon. A photography enthusiast and an author, Shailendra has successfully exhibited his landscape photographs, primarily taken in the Southern part of India.
“Acerca de” puede pertenecer a otra edición de este título.
PBShop.store US
Wood Dale, IL, Estados Unidos de America
Vendedor de IberLibro desde 7 de abril de 2005
Tarifas de envío en Estados Unidos de America
| Artículo | De 7 a 14 días hábiles | De 7 a 14 días hábiles |
|---|---|---|
| Primer artículo | EUR 0,00 | EUR 0,00 |
Métodos de pago
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