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Blueprints for Text Analytics Using Python : Machine Learning-Based Solutions for Common Real World (NLP) Applications by Jens Albrecht, Sidharth Ramachandran and Christian Winkler (2021, Trade Paperback)
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About this product
Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-10149207408X
ISBN-139781492074083
eBay Product ID (ePID)17050378543
Product Key Features
Number of Pages350 Pages
Publication NameBlueprints for Text Analytics Using Python : Machine Learning-Based Solutions for Common Real World (NLP) Applications
LanguageEnglish
SubjectData Modeling & Design, General, Databases / Data Mining
Publication Year2021
TypeTextbook
AuthorJens Albrecht, Sidharth Ramachandran, Christian Winkler
Subject AreaMathematics, Computers
FormatTrade Paperback
Dimensions
Item Height0.9 in
Item Weight25.5 Oz
Item Length9.2 in
Item Width7.7 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2022-276251
Dewey Edition23
IllustratedYes
Dewey Decimal006.35
SynopsisTurning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order. This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly. Extract data from APIs and web pages Prepare textual data for statistical analysis and machine learning Use machine learning for classification, topic modeling, and summarization Explain AI models and classification results Explore and visualize semantic similarities with word embeddings Identify customer sentiment in product reviews Create a knowledge graph based on named entities and their relations
LC Classification NumberQA76.9.N38
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