Udemy - Natural Language Processing with Python: from zero to hero [TP]

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[Tutorialsplanet.NET] Udemy - Natural Language Processing with Python from zero to hero 1. Introduction to the course
  • 1. IntroWhat You Will Learn.mp4 (13.2 MB)
  • 1. IntroWhat You Will Learn.vtt (1.9 KB)
  • 1.1 Full Source Code.html (0.1 KB)
  • 2. Installing Python.mp4 (13.4 MB)
  • 2. Installing Python.vtt (1.4 KB)
  • 3. Jupyter Notebook Intro.mp4 (7.6 MB)
  • 3. Jupyter Notebook Intro.vtt (2.4 KB)
  • 4. Natural Language Processing Foundations.html (11.5 KB)
  • 5. Python NLP Libraries and Packages.mp4 (35.7 MB)
  • 5. Python NLP Libraries and Packages.vtt (5.0 KB)
2. Text Processing and NLP Techniques
  • 1. Installing, Importing, and Viewing NLTK Package.mp4 (40.9 MB)
  • 1. Installing, Importing, and Viewing NLTK Package.vtt (6.9 KB)
  • 2. Tokenization.mp4 (167.0 MB)
  • 2. Tokenization.vtt (12.7 KB)
  • 3. Stemming and Lemmatization.mp4 (80.6 MB)
  • 3. Stemming and Lemmatization.vtt (5.6 KB)
  • 4. Part-of-Speech Tagging (POS Tagging).mp4 (13.4 MB)
  • 4. Part-of-Speech Tagging (POS Tagging).vtt (2.5 KB)
  • 5. Named Entity Recognition.mp4 (59.9 MB)
  • 5. Named Entity Recognition.vtt (7.9 KB)
3. NLP Techniques and Algorithms
  • 1. N-grams.mp4 (65.8 MB)
  • 1. N-grams.vtt (10.0 KB)
  • 2. Bag of Words - Term Frequency.mp4 (162.7 MB)
  • 2. Bag of Words - Term Frequency.vtt (18.6 KB)
  • 3. word2vec.mp4 (101.7 MB)
  • 3. word2vec.vtt (16.7 KB)
  • 3.1 word2vec.png.png (142.2 KB)
  • 3.2 bbc-fulltext (1).zip.zip (2.7 MB)
  • 4. Topic Modeling (LDA).mp4 (63.0 MB)
  • 4. Topic Modeling (LDA).vtt (8.7 KB)
4. Case Studies
  • 1. Document Similarity.mp4 (123.0 MB)
  • 1. Document Similarity.vtt (14.9 KB)
  • [Tutorialsplanet.NET].url (0.1 KB)

Description

Udemy - Natural Language Processing with Python: from zero to hero [TP]

learn how to process text, utilize nlp algorithms, and bring all of that knowledge together by doing a case study!

For more Udemy Courses: https://tutorialsplanet.net



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Udemy - Natural Language Processing with Python: from zero to hero [TP]
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