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Natural Language Processing Python And Nltk Nitin Hardeniya

  • SKU: BELL-5684832
Natural Language Processing Python And Nltk Nitin Hardeniya
$ 31.00 $ 45.00 (-31%)

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Natural Language Processing Python And Nltk Nitin Hardeniya instant download after payment.

Publisher: Packt Publishing
File Extension: PDF
File size: 11.37 MB
Pages: 755
Author: Nitin Hardeniya
Language: English
Year: 2016

Product desciption

Natural Language Processing Python And Nltk Nitin Hardeniya by Nitin Hardeniya instant download after payment.

Natural Language Processing is a field of computational linguistics and artificial intelligence that deals with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. The number of human-computer interaction instances are increasing so its becoming imperative that computers comprehend all major natural languages. The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy. The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods. The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python.

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