| 1. Accessing Corpora.mp4 | 17.9 MB | ||
| 1. Accessing Corpora.srt | 10.5 KB | ||
| 1. Conclusion.mp4 | 3.4 MB | ||
| 1. Conclusion.srt | 2.9 KB | ||
| 1. Do you want to learn a specific NLTK or NLP topic.html | 307.2 B | ||
| 1. Edit Distance Example.mp4 | 14.8 MB | ||
| 1. Edit Distance Example.srt | 2.1 KB | ||
| 1. Information Extraction Architecture.mp4 | 5.8 MB | ||
| 1. Information Extraction Architecture.srt | 4.5 KB | ||
| 1. Introduction to NLP.mp4 | 12.9 MB | ||
| 1. Introduction to NLP.srt | 9.2 KB | ||
| 1. Machine Learning Overview.mp4 | 8.8 MB | ||
| 1. Machine Learning Overview.srt | 8 KB | ||
| 1. More NLP Tutorials.html | 512 B | ||
| 1. NLP Pipeline.mp4 | 31.8 MB | ||
| 1. NLP Pipeline.srt | 8.2 KB | ||
| 1. Tagger.mp4 | 18.6 MB | ||
| 1. Tagger.srt | 13.4 KB | ||
| 1.1 accessing-corpora.py | 1.1 KB | ||
| 1.1 tagger.py | 512 B | ||
| 2. Chunking Overiew.mp4 | 5.6 MB | ||
| 2. Chunking Overiew.srt | 4.8 KB | ||
| 2. Course Technical Requirements.html | 2.4 KB | ||
| 2. Edit Distance - Spelling Checker.mp4 | 9.1 MB | ||
| 2. Edit Distance - Spelling Checker.srt | 1.4 KB | ||
| 2. Loading Your Own Corpus.mp4 | 12.7 MB | ||
| 2. Loading Your Own Corpus.srt | 9.9 KB | ||
| 2. Logic Of Naive Bayes.mp4 | 39.8 MB | ||
| 2. Logic Of Naive Bayes.srt | 30.9 KB | ||
| 2. Tagged Corpus.mp4 | 20.8 MB | ||
| 2. Tagged Corpus.srt | 12.8 KB | ||
| 2. Tokenization.mp4 | 33.7 MB | ||
| 2. Tokenization.srt | 10.5 KB | ||
| 2. What's Next for You.html | 2.1 KB | ||
| 2.1 loading-your-own-corpus.py | 409.6 B | ||
| 2.1 requirements.txt | 0 B | ||
| 2.1 shakespeare-taming-of-the-shrew.txt | 121.2 KB | ||
| 2.1 tagged-corpus.py | 614.4 B | ||
| 2.2 shakespeare-taming-of-the-shrew.txt | 121.2 KB | ||
| 2.2 tokenization.py | 614.4 B | ||
| 3. Appendix List of Correct Words for Spelling Checkers.html | 1 KB | ||
| 3. Chunking in Coding.mp4 | 6 MB | ||
| 3. Chunking in Coding.srt | 5.4 KB | ||
| 3. Conditional Frequency Distribution.mp4 | 30.7 MB | ||
| 3. Conditional Frequency Distribution.srt | 18.5 KB | ||
| 3. Installing and Setting Up NLTK.mp4 | 6 MB | ||
| 3. Installing and Setting Up NLTK.srt | 5.4 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. The Default Tagger.mp4 | 24.4 MB | ||
| 3. The Default Tagger.srt | 14.3 KB | ||
| 3. What is Token.html | 102.4 B | ||
| 3.1 conditional-frequency-distribution.py | 614.4 B | ||
| 3.1 default-tagger.py | 409.6 B | ||
| 3.1 gender-application-part1.py | 1.8 KB | ||
| 3.1 noun-phrase-chunking.py | 307.2 B | ||
| 3.1 requirements.txt | 0 B | ||
| 3.2 setting-up.py | 0 B | ||
| 4. Edit Distance - Plagiarism Checker Translation Memory.mp4 | 16.2 MB | ||
| 4. Edit Distance - Plagiarism Checker Translation Memory.srt | 1.9 KB | ||
| 4. Exercise Named Entity Recognition.html | 1.2 KB | ||
| 4. Lexical Resources Vocabulary.mp4 | 25.5 MB | ||
| 4. Lexical Resources Vocabulary.srt | 17.3 KB | ||
| 4. NLTK Accessing Texts.mp4 | 9 MB | ||
| 4. NLTK Accessing Texts.srt | 6.4 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. Regexp Tagger.mp4 | 24.2 MB | ||
| 4. Regexp Tagger.srt | 14.5 KB | ||
| 4. Regular Expressions.mp4 | 26.2 MB | ||
| 4. Regular Expressions.srt | 15.5 KB | ||
| 4.1 accessing-texts.py | 204.8 B | ||
| 4.1 gender-application-part2.py | 1.7 KB | ||
| 4.1 lexical-resources-vocabulary.py | 512 B | ||
| 4.1 regex-tagger.py | 307.2 B | ||
| 4.1 regular-expressions.py | 512 B | ||
| 5. Applications of Regex.mp4 | 18.3 MB | ||
| 5. Applications of Regex.srt | 14.9 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. Chinking.mp4 | 14.6 MB | ||
| 5. Chinking.srt | 13.9 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. Terminology.html | 3.5 KB | ||
| 5. Unigram Tagger.mp4 | 16.8 MB | ||
| 5. Unigram Tagger.srt | 13.9 KB | ||
| 5.1 applications-of-regex.py | 512 B | ||
| 5.1 basic-functions.py | 1.2 KB | ||
| 5.1 chinking.py | 409.6 B | ||
| 5.1 gender-application-part3.py | 2.6 KB | ||
| 5.1 unigram-tagging.py | 716.8 B | ||
| 6. NLP Basic Terminology.html | 102.4 B | ||
| 6. Ngram Tagger.mp4 | 19.5 MB | ||
| 6. Ngram Tagger.srt | 16 KB | ||
| 6. Project #2 Document Classifier Application.mp4 | 65.1 MB | ||
| 6. Project #2 Document Classifier Application.srt | 29.5 KB | ||
| 6. Stanford NLP API.mp4 | 10 MB | ||
| 6. Stanford NLP API.srt | 9.3 KB | ||
| 6. Stemming.mp4 | 13.7 MB | ||
| 6. Stemming.srt | 12.7 KB | ||
| 6. Summary NLTK Basic Functions.html | 2.7 KB | ||
| 6.1 document-classifier.py | 1 KB | ||
| 6.1 n-gram tagging.py | 409.6 B | ||
| 6.1 stemming.py | 512 B | ||
| 7. Chunking and Chinking.html | 102.4 B | ||
| 7. Frequency Distribution with NLTK.mp4 | 16.6 MB | ||
| 7. Frequency Distribution with NLTK.srt | 12.2 KB | ||
| 7. Lemmatization.mp4 | 25.8 MB | ||
| 7. Lemmatization.srt | 13.1 KB | ||
| 7. POS Tagging.html | 102.4 B | ||
| 7.1 Frequency-distributions.py | 307.2 B | ||
| 7.1 lemmatization.py | 614.4 B | ||
| 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. Regex for Tokenization.mp4 | 20.7 MB | ||
| 8. Regex for Tokenization.srt | 13.8 KB | ||
| 8.1 personal-frequency-distribution.py | 307.2 B | ||
| 8.1 regex-for-tokenization.py | 614.4 B | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 121 total files | |||
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.
| torrent name | size | uploader | age | seed | leech |
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| 2.2 GB | tutsnode | 5 years | 0 | 0 | |
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[ DevCourseWeb ] Udemy - Python Data Analysis Bootcamp with Pandas and NLTK Posted by
freecoursewb in Other
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2.1 GB | freecoursewb | 5 years | 0 | 0 |
| 110.7 MB | tutsgalaxy | 8 years | 15 | 2 |
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