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...is a collective term for characteristics that the two institutions share.
█ Natural Language Processing (NLP) Using Python
█
▄
█ by Abhishek Chhibber
█ Publisher: Technics Publications
▓ Release Date: September 2017
▓ Running time: 3:26:35
▒ Topic: Python
▒
░ Scale: 13x50mb
░ Downloads: ( ) Included (■) Without
░
░ Lecture Date: 09/2017
░ Lecture Link: https://goo.gl/D4R4wK
░
:
| This series will provide an overview and working knowledge of
| Natural Language Processing (NLP), using PythonÆs Natural Language
| Toolkit (NLTK) library within an Anaconda environment. It is
| intended for users who have basic programming knowledge of Python
| and want to start with NLP.
|
| The tutorial starts with an introduction to data structures and
| regular expressions, then progresses to accessing and analyzing
| text using NLTK, and finally graduating to making predictions on
| text using PythonÆs machine learning module, Scikit Learn. Topics
| covered in this video include:
|
| Setting up the Environment. After providing an overview to this
| video series, this clip shows you how to install and run Python,
| as well as Anaconda and the necessary libraries (including NLTK).
| Manipulating Data. Explores how to manipulate data in Python,
| using these data structures: strings, lists, tuples, dictionaries,
| and sets.
| Using Regular Expressions (Regex). Explores using Regular
| Expressions (Regex) in Python including creating a Regex grammar,
| using Search and FindAll methods, using special characters in
| Regex, and applying pattern-matching and string-substitution.
| Accessing Files and Reading Text. Covers the ways of accessing
| files and reading text, including retrieving directories, reading
| text (.txt) files, reading MS Word (.docx) documents, reading .pdf
| files, and reading and accessing NLTK corpora.
| Extracting, Cleaning, and Preprocessing Text, Part 1. Explores
| extracting, cleaning and preprocessing text, using sentence and
| word tokenization, bigrams, trigrams, and ngrams, stemming,
| lemmatization, and stop-word removal.
| Extracting, Cleaning, and Preprocessing Text, Part 2. Covers the
| process of extracting, cleaning, and preprocessing text, using
| Part of Speech (POS) tagging, and named entity recognition.
| Analyzing Sentence Structure. Explains how to analyze a sentence
| structure, including using syntax trees, chunking of words,
| chinking of words, and context-free grammar (CFG).
| Classifying Text, Part 1. Covers text classification using machine
| learning, including understanding the concepts of bag of words,
| CountVectorizer, and Term Frequency - Inverse Document Frequency
| (TF-IDF).
| Classifying Text, Part 2. Explores text classification using
| machine learning, including converting text to features and
| labels, using Multinomial Na∩ve Bayes Classifier, and leveraging
| the confusion matrix.
| Putting the Pieces Together: NLP Project on Sentiment Analysis.
| Implements everything we have learned so far on a data set. This
| full NLP project summarizes topics discussed in the previous
| tutorials to create the machine learning classifier in performing
| sentiment analysis.
,