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Natural Language Processing Recipes - Unlocking Text Data with Machine Learning and Deep Learning Using Python (Paperback, 2nd ed.)
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Natural Language Processing Recipes - Unlocking Text Data with Machine Learning and Deep Learning Using Python (Paperback, 2nd ed.)
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Focus on implementing end-to-end projects using Python and leverage
state-of-the-art algorithms. This book teaches you to efficiently
use a wide range of natural language processing (NLP) packages to:
implement text classification, identify parts of speech, utilize
topic modeling, text summarization, sentiment analysis, information
retrieval, and many more applications of NLP. The book begins with
text data collection, web scraping, and the different types of data
sources. It explains how to clean and pre-process text data, and
offers ways to analyze data with advanced algorithms. You then
explore semantic and syntactic analysis of the text. Complex NLP
solutions that involve text normalization are covered along with
advanced pre-processing methods, POS tagging, parsing, text
summarization, sentiment analysis, word2vec, seq2seq, and much
more. The book presents the fundamentals necessary for applications
of machine learning and deep learning in NLP. This second edition
goes over advanced techniques to convert text to features such as
Glove, Elmo, Bert, etc. It also includes an understanding of how
transformers work, taking sentence BERT and GPT as examples. The
final chapters explain advanced industrial applications of NLP with
solution implementation and leveraging the power of deep learning
techniques for NLP problems. It also employs state-of-the-art
advanced RNNs, such as long short-term memory, to solve complex
text generation tasks. After reading this book, you will have a
clear understanding of the challenges faced by different industries
and you will have worked on multiple examples of implementing NLP
in the real world. What You Will Learn Know the core concepts of
implementing NLP and various approaches to natural language
processing (NLP), including NLP using Python libraries such as
NLTK, textblob, SpaCy, Standford CoreNLP, and more Implement text
pre-processing and feature engineering in NLP, including advanced
methods of feature engineering Understand and implement the
concepts of information retrieval, text summarization, sentiment
analysis, text classification, and other advanced NLP techniques
leveraging machine learning and deep learning Who This Book Is For
Data scientists who want to refresh and learn various concepts of
natural language processing (NLP) through coding exercises
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