Udemy - Text Mining and Natural Language Processing in Python

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size607.7 MB
  • Uploaded Byfreecoursewb
  • Downloads84
  • Last checkedMar. 06th '22
  • Date uploadedMar. 04th '22
  • Seeders 10
  • Leechers8

Infohash : 6EEFB8FC67571AFA09D4ACC887D4CD2AE88A8D09

Text Mining and Natural Language Processing in Python



https://DevCourseWeb.com

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 28 lectures (1h 48m) | Size: 407.4 MB

Learn the basics of Natural Language Processing in Python and build your own Deep Learning Sentiment Analysis!

What you'll learn
Students will be able to install Jupyter Notebook and manage Python Modules
Definition of Natural Language and its Applications
Get to know Basics of Natural Language Processing
Learn Basics of Text Processing with NLTK and spaCy
Get to know Traditional Feature Engineering Models
Implement a working Sentiment Analysis Model
Learn to Code all these points in Python

Requirements
Prior Experience in Python
Prior Implementation of Machine Learning Models will be beneficial
Should have an Interest in Learning Practical Text Mining and Natural Language Processing (NLP)

Files:

[ DevCourseWeb.com ] Udemy - Text Mining and Natural Language Processing in Python
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction to the Course Jupyter Notebook and Python Modules
    • 1. Install Jupyter Notebook.mp4 (28.7 MB)
    • 2. Module Management in Jupyter Notebook.mp4 (23.1 MB)
    2. Natural Language Basics
    • 1. Natural Language.mp4 (25.4 MB)
    • 2. Natural Language Processing and Applications.mp4 (20.6 MB)
    3. Processing Text
    • 1. NLTK and spaCy.mp4 (20.2 MB)
    • 2. Tokenization.mp4 (9.4 MB)
    • 3. Tokenization in Python.mp4 (19.2 MB)
    • 4. Text Cleaning & Case Conversions.mp4 (15.3 MB)
    • 5. Text Cleaning & Case Conversions in Python.mp4 (36.0 MB)
    • 6. Stemming & Lemmatization.mp4 (20.2 MB)
    • 7. Stemming & Lemmatization in Python.mp4 (13.4 MB)
    • 8. Stopwords.mp4 (7.2 MB)
    • 9. Stopwords in Python.mp4 (21.7 MB)
    4. Traditional Feature Engineering Models
    • 1. Bag of Words Model.mp4 (23.0 MB)
    • 2. Bag of N-Grams Model.mp4 (15.1 MB)
    • 3. Bag of N-Grams Model in Python.mp4 (34.5 MB)
    • 4. Word2Vec.mp4 (19.0 MB)
    • 5. Word2Vec in Python.mp4 (25.4 MB)
    • 6. BERT Embeddings.mp4 (24.6 MB)
    • 7. BERT Embeddings in Python.mp4 (37.8 MB)
    5. Recap Machine Learning
    • 1. Convolutional Neural Networks for Classification.mp4 (27.4 MB)
    6. Text Classification using TensorFlow
    • 1. Python Modules.mp4 (16.3 MB)
    • 2. Dataset.mp4 (15.2 MB)
    • 3. Text Preprocessing.mp4 (22.9 MB)
    • 4. Tokenizing.mp4 (9.5 MB)
    • 5. Preparing Batches.mp4 (25.7 MB)
    • 6. Explaining the Model.mp4 (40.1 MB)
    • 7. Test the Model.mp4 (10.9 MB)
    • Bonus Resources.txt (0.4 KB)

Code:

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