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Practical natural language processing with Pythonwith case studies from industries using text data at scale /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Practical natural language processing with Pythonby Mathangi Sri.
Reminder of title:	with case studies from industries using text data at scale /
Author:	Sri, Mathangi.
Published:	Berkeley, CA :Apress :2021.
Description:	xv, 253 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-6246-7
ISBN:	9781484262467$q(electronic bk.)

Practical natural language processing with Pythonwith case studies from industries using text data at scale /
Sri, Mathangi.

Practical natural language processing with Pythonwith case studies from industries using text data at scale /[electronic resource] :by Mathangi Sri. - Berkeley, CA :Apress :2021. - xv, 253 p. :ill., digital ;24 cm.

Chapter 1: Text Data in Real Word -- Chapter 2: NLP in Customer Service -- Chapter 3: NLP in Online Reviews -- Chapter 4: NLP in BFSI -- Chapter 5: NLP in Virtual Assistants.

Work with natural language tools and techniques to solve real-world problems. This book focuses on how natural language processing (NLP) is used in various industries. Each chapter describes the problem and solution strategy, then provides an intuitive explanation of how different algorithms work and a deeper dive on code and output in Python. Practical Natural Language Processing with Python follows a case study-based approach. Each chapter is devoted to an industry or a use case, where you address the real business problems in that industry and the various ways to solve them. You start with various types of text data before focusing on the customer service industry, the type of data available in that domain, and the common NLP problems encountered. Here you cover the bag-of-words model supervised learning technique as you try to solve the case studies. Similar depth is given to other use cases such as online reviews, bots, finance, and so on. As you cover the problems in these industries you'll also cover sentiment analysis, named entity recognition, word2vec, word similarities, topic modeling, deep learning, and sequence to sequence modelling. By the end of the book, you will be able to handle all types of NLP problems independently. You will also be able to think in different ways to solve language problems. Code and techniques for all the problems are provided in the book. You will: Build an understanding of NLP problems in industry Gain the know-how to solve a typical NLP problem using language-based models and machine learning Discover the best methods to solve a business problem using NLP - the tried and tested ones Understand the business problems that are tough to solve.

ISBN: 9781484262467$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-6246-7doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: TK7882.P3 / S65 2021

Dewey Class. No.: 006.31

Practical natural language processing with Pythonwith case studies from industries using text data at scale /
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