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001434265 0247_ $$a10.1007/978-1-4842-6246-7.$$2doi
001434265 035__ $$aSP(OCoLC)1238206192
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001434265 1001_ $$aSri, Mathangi.,$$eauthor.$$4aut$$4http://id.loc.gov/vocabulary/relators/aut
001434265 24510 $$aPractical Natural Language Processing with Python:$$bWith Case Studies from Industries Using Text Data at Scale /$$cby Mathangi Sri.
001434265 250__ $$a1st ed. 2021.
001434265 264_1 $$aBerkeley, CA :$$bApress :$$bImprint: Apress,$$c2021.
001434265 300__ $$a1 online resource (XV, 253 p. 103 illus.)
001434265 336__ $$atext$$btxt$$2rdacontent
001434265 337__ $$acomputer$$bc$$2rdamedia
001434265 338__ $$aonline resource$$bcr$$2rdacarrier
001434265 347__ $$atext file$$bPDF$$2rda
001434265 4900_ $$aITpro collection
001434265 504__ $$aIncludes bibliographical references.
001434265 5050_ $$aChapter 1: Text Data in Real Word -- Chapter 2: NLP in Customer Service -- Chapter 3: NLP in Online Reviews -- Chapter 4: NLP in BFSI -- Chapter 5: NLP in Virtual Assistants.
001434265 506__ $$aAccess limited to authorized users.
001434265 520__ $$aWork with natural language tools and techniques to solve real-world problems. This book focuses on how natural language processing (NLP) is used in various industries. Each chapter describes the problem and solution strategy, then provides an intuitive explanation of how different algorithms work and a deeper dive on code and output in Python. Practical Natural Language Processing with Python follows a case study-based approach. Each chapter is devoted to an industry or a use case, where you address the real business problems in that industry and the various ways to solve them. You start with various types of text data before focusing on the customer service industry, the type of data available in that domain, and the common NLP problems encountered. Here you cover the bag-of-words model supervised learning technique as you try to solve the case studies. Similar depth is given to other use cases such as online reviews, bots, finance, and so on. As you cover the problems in these industries you'll also cover sentiment analysis, named entity recognition, word2vec, word similarities, topic modeling, deep learning, and sequence to sequence modelling. By the end of the book, you will be able to handle all types of NLP problems independently. You will also be able to think in different ways to solve language problems. Code and techniques for all the problems are provided in the book. You will: Build an understanding of NLP problems in industry Gain the know-how to solve a typical NLP problem using language-based models and machine learning Discover the best methods to solve a business problem using NLP - the tried and tested ones Understand the business problems that are tough to solve .
001434265 650_0 $$aMachine learning.
001434265 650_0 $$aPython (Computer program language)
001434265 650_0 $$aOpen source software.
001434265 650_0 $$aComputer programming.
001434265 650_6 $$aApprentissage automatique.
001434265 650_6 $$aPython (Langage de programmation)
001434265 650_6 $$aLogiciels libres.
001434265 650_6 $$aProgrammation (Informatique)
001434265 655_0 $$aElectronic books.
001434265 77608 $$z1-4842-6245-X
001434265 852__ $$bebk
001434265 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-6246-7$$zOnline Access$$91397441.1
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001434265 980__ $$aBIB
001434265 980__ $$aEBOOK
001434265 982__ $$aEbook
001434265 983__ $$aOnline
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