Practical natural language processing par sri mathangi (20 résultats)

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Etat : New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

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Etat : New. 1st ed. edition NO-PA16APR2015-KAP.

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Paperback. Etat : Brand New. 253 pages. 9.75x7.00x0.75 inches. In Stock.

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Taschenbuch. Etat : Neu. Practical Natural Language Processing with Python | With Case Studies from Industries Using Text Data at Scale | Mathangi Sri | Taschenbuch | xv | Englisch | 2020 | Apress | EAN 9781484262450 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 theproblems 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.What You Will LearnBuild an understanding of NLP problems in industryGain the know-how to solve a typical NLP problem using language-based models and machine learningDiscover the best methods to solve a business problem using NLP - the tried and tested onesUnderstand the business problems that are tough to solveWho This Book Is ForAnalytics and data science professionals who want to kick start NLP, and NLP professionals who want to get new ideas to solve theproblems at hand. 272 pp. Englisch.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 theproblems 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.What You Will LearnBuild an understanding of NLP problems in industryGain the know-how to solve a typical NLP problem using language-based models and machine learningDiscover the best methods to solve a business problem using NLP - the tried and tested onesUnderstand the business problems that are tough to solveWho This Book Is ForAnalytics and data science professionals who want to kick start NLP, and NLP professionals who want to get new ideas to solve theproblems at hand.…

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Emphasizes a data- and business problem-first approachA case study-based approach that presents real-world problems and solutionsExplains the accuracy and limitations of certain libraries from a profe.…

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 theproblems 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.What You Will LearnBuild an understanding of NLP problems in industryGain the know-how to solve a typical NLP problem using language-based models and machine learningDiscover the best methods to solve a business problem using NLP - the tried and tested onesUnderstand the business problems that are tough to solveWho This Book Is ForAnalytics and data science professionals who want to kick start NLP, and NLP professionals who want to get new ideas to solve theproblems at hand.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 272 pp. Englisch.…