Udemy - NLTK - Build Document Classifier and Spell Checker with Python

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[ DevCourseWeb.com ] Udemy - NLTK - Build Document Classifier and Spell Checker with Python
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Getting Started with NLTK (Natural Language Processing Toolkit)
    • 1. Introduction to NLP.mp4 (12.9 MB)
    • 1. Introduction to NLP.srt (9.2 KB)
    • 2. Course Technical Requirements.html (2.4 KB)
    • 2.1 requirements.txt (0.0 KB)
    • 3. Installing and Setting Up NLTK.mp4 (6.0 MB)
    • 3. Installing and Setting Up NLTK.srt (5.4 KB)
    • 3.1 requirements.txt (0.0 KB)
    • 3.2 setting-up.py (0.0 KB)
    • 4. NLTK Accessing Texts.mp4 (9.0 MB)
    • 4. NLTK Accessing Texts.srt (6.4 KB)
    • 4.1 accessing-texts.py (0.2 KB)
    • 5. Basic Functions concordance, similar, dispersion_plot, count.mp4 (22.9 MB)
    • 5. Basic Functions concordance, similar, dispersion_plot, count.srt (16.2 KB)
    • 5.1 basic-functions.py (1.2 KB)
    • 6. Summary NLTK Basic Functions.html (2.7 KB)
    • 7. Frequency Distribution with NLTK.mp4 (16.6 MB)
    • 7. Frequency Distribution with NLTK.srt (12.2 KB)
    • 7.1 Frequency-distributions.py (0.3 KB)
    • 8. Frequency Distribution on Your Text with NLTK.mp4 (8.6 MB)
    • 8. Frequency Distribution on Your Text with NLTK.srt (7.4 KB)
    • 8.1 personal-frequency-distribution.py (0.3 KB)
    10. Bonus Material
    • 1. More NLP Tutorials.html (0.5 KB)
    • 2. What's Next for You.html (2.1 KB)
    2. Do you want to learn a specific NLP topic
    • 1. Do you want to learn a specific NLTK or NLP topic.html (0.3 KB)
    3. Corpora
    • 1. Accessing Corpora.mp4 (17.9 MB)
    • 1. Accessing Corpora.srt (10.5 KB)
    • 1.1 accessing-corpora.py (1.1 KB)
    • 2. Loading Your Own Corpus.mp4 (12.7 MB)
    • 2. Loading Your Own Corpus.srt (9.9 KB)
    • 2.1 loading-your-own-corpus.py (0.4 KB)
    • 2.2 shakespeare-taming-of-the-shrew.txt (121.2 KB)
    • 3. Conditional Frequency Distribution.mp4 (30.7 MB)
    • 3. Conditional Frequency Distribution.srt (18.5 KB)
    • 3.1 conditional-frequency-distribution.py (0.6 KB)
    • 4. Lexical Resources Vocabulary.mp4 (25.5 MB)
    • 4. Lexical Resources Vocabulary.srt (17.3 KB)
    • 4.1 lexical-resources-vocabulary.py (0.5 KB)
    • 5. Terminology.html (3.5 KB)
    • 6. NLP Basic Terminology.html (0.1 KB)
    4. Processing Raw Text with NLTK
    • 1. NLP Pipeline.mp4 (31.8 MB)
    • 1. NLP Pipeline.srt (8.2 KB)
    • 2. Tokenization.mp4 (33.7 MB)
    • 2. Tokenization.srt (10.5 KB)
    • 2.1 shakespeare-taming-of-the-shrew.txt (121.2 KB)
    • 2.2 tokenization.py (0.6 KB)
    • 3. What is Token.html (0.1 KB)
    • 4. Regular Expressions.mp4 (26.2 MB)
    • 4. Regular Expressions.srt (15.5 KB)
    • 4.1 regular-expressions.py (0.5 KB)
    • 5. Applications of Regex.mp4 (18.3 MB)
    • 5. Applications of Regex.srt (14.9 KB)
    • 5.1 applications-of-regex.py (0.5 KB)
    • 6. Stemming.mp4 (13.7 MB)
    • 6. Stemming.srt (12.7 KB)
    • 6.1 stemming.py (0.5 KB)
    • 7. Lemmatization.mp4 (25.8 MB)
    • 7. Lemmatization.srt (13.1 KB)
    • 7.1 lemmatization.py (0.6 KB)
    • 8. Regex for Tokenization.mp4 (20.7 MB)
    • 8. Regex for Tokenization.srt (13.8 KB)
    • 8.1 regex-for-tokenization.py (0.6 KB)
    5. Categorizing and Tagging Words with NLTK
    • 1. Tagger.mp4 (18.6 MB)
    • 1. Tagger.srt (13.4 KB)
    • 1.1 tagger.py (0.5 KB)
    • 2. Tagged Corpus.mp4 (20.8 MB)
    • 2. Tagged Corpus.srt (12.8 KB)
    • 2.1 tagged-corpus.py (0.6 KB)
    • 3. The Default Tagger.mp4 (24.4 MB)
    • 3. The Default Tagger.srt (14.3 KB)
    • 3.1 default-tagger.py (0.4 KB)
    • 4. Regexp Tagger.mp4 (24.2 MB)
    • 4. Regexp Tagger.srt (14.5 KB)
    • 4.1 regex-tagger.py (0.3 KB)
    • 5. Unigram Tagger.mp4 (16.8 MB)
    • 5. Unigram Tagger.srt (13.9 KB)
    • 5.1 unigram-tagging.py (0.7 KB)
    • 6. Ngram Tagger.mp4 (19.5 MB)
    • 6. Ngram Tagger.srt (16.0 KB)
    • 6.1 n-gram tagging.py (0.4 KB)
    • 7. POS Tagging.html (0.1 KB)
    6. Sentiment Analysis Text Classification Practical Projects
    • 1. Machine Learning Overview.mp4 (8.8 MB)
    • 1. Machine Learning Overview.srt (8.0 KB)
    • 2. Logic Of Naive Bayes.mp4 (39.8 MB)
    • 2. Logic Of Naive Bayes.srt (30.9 KB)
    • 3. Project #1 Gender Prediction Application - Part 1.mp4 (43.4 MB)
    • 3. Project #1 Gender Prediction Application - Part 1.srt (34.5 KB)
    • 3.1 gender-application-part1.py (1.8 KB)
    • 4. Project #1 Gender Prediction Application - Part 2.mp4 (27.2 MB)
    • 4. Project #1 Gender Prediction Application - Part 2.srt (17.3 KB)
    • 4.1 gender-application-part2.py (1.7 KB)
    • 5. Project #1 Gender Prediction Application - Part 3.mp4 (36.5 MB)
    • 5. Project #1 Gender Prediction Application - Part 3.srt (20.7 KB)
    • 5.1 gender-application-part3.py (2.6 KB)
    • 6. Project #2 Document Classifier Application.mp4 (65.1 MB)
    • 6. Project #2 Document Classifier Application.srt (29.5 KB)
    • 6.1 document-classifier.py (1.0 KB)
    7. Extracting Info from Text
    • 1. Information Extraction Architecture.mp4 (5.8 MB)
    • 1. Information Extraction Architecture.srt (4.5 KB)
    • 2. Chunking Overiew.mp4 (5.6 MB)
    • 2. Chunking Overiew.srt (4.8 KB)
    • 3. Chunking in Coding.mp4 (6.0 MB)
    • 3. Chunking in Coding.srt (5.4 KB)
    • 3.1 noun-phrase-chunking.py (0.3 KB)
    • 4. Exercise Named Entity Recognition.html (1.2 KB)
    • 5. Chinking.mp4 (14.6 MB)
    • 5. Chinking.srt (13.9 KB)
    • 5.1 chinking.py (0.4 KB)
    • 6. Stanford NLP API.m

Description

NLTK: Build Document Classifier & Spell Checker with Python

https://DevCourseWeb.com

Last updated 2/2019
Created by GoTrained Academy,Waqar Ahmed
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 46 Lectures ( 5h 17m ) | Size: 764 MB

NLP with Python - Analyzing Text with the Natural Language Toolkit (NLTK) - Natural Language Processing (NLP) Tutorial

What you'll learn:
NLTK Main Functions: Concordance, Similar, Lexical Dispersion Plot
Text Tokenization
Text Normalization: Stemming & Lemmatization
Text Tagging: Unigram, N-Gram, Regex
Text Classification
Project 1: Gender Prediction Application
Project 2: Document Classification Application
Information Extraction from Text: Chunking, Chinking, Name Entity Recognition
Source Code *.py Files of All Lectures
English Captions for All Lectures
Q&A board to send your questions and get them answered quickly

Requirements:
Good Python level. This Natural Language Processing (NLP) tutorial assumes that you already familiar with the basics of writing simple Python programs and that you are generally familiar with Python's core features (data structures, file handling, functions, classes, modules, common library modules, etc.).
Python 3.4+ (or 2.7). Please note that the tutorial codes are written in Python 3, but it is up to you to fine-tune them if you want to run them on Python 2.



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Udemy - NLTK - Build Document Classifier and Spell Checker with Python

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Download torrent
764.2 MB
seeders:9
leechers:8
Udemy - NLTK - Build Document Classifier and Spell Checker with Python

Torrent hash: 14FFC92314692FE1E0933C5251B0EEB8DC289014