Udemy - AI with Python - Natural Language Processing (NLP) (Updated)

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[ TutSala.com ] Udemy - AI with Python - Natural Language Processing (NLP) (Updated)
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Welcome
    • 1. Introduction.mp4 (13.3 MB)
    • 1. Introduction.srt (3.7 KB)
    10. Summary
    • 1. Course Summary.mp4 (12.3 MB)
    • 1. Course Summary.srt (4.0 KB)
    • 2. Course Material & Source Code.html (0.0 KB)
      • Working Files 01
      • No Work Files.txt (0.0 KB)
      02
      • 1 Unicode Python 2.ipynb (5.6 KB)
      • 2 Unicode Python 3.ipynb (1.7 KB)
      • string processing.ipynb (7.1 KB)
      03
      • sentences NLTK spaCy.ipynb (3.4 KB)
      • stop words in NLTK.ipynb (6.3 KB)
      • tokens NLTK spaCy.ipynb (7.9 KB)
      04
      • Train Word Vectors2.ipynb (23.5 KB)
      • Use Google Word Vectors.ipynb (29.4 KB)
      • tf-idf Gensim.ipynb (11.2 KB)
      05
      • Dependency Parsing.ipynb (5.6 KB)
      • Entity Recognition.ipynb (4.5 KB)
      • Head of a Sentence.ipynb (2.9 KB)
      06
      • Sentiment_VADER.ipynb (4.2 KB)
      07
      • Text_Classification_With_TextBlob.ipynb (14.5 KB)
      • Text_Classification_with_scikit-learn.ipynb (88.6 KB)
      • our_textblob_classifiers.py (20.1 KB)
      08
      • LDA_with_gensim.ipynb (21.7 KB)
      • pyLDAvis.ipynb (155.4 KB)
      09
      • FRED.ipynb (2.8 KB)
      • pyspotlight.ipynb (3.1 KB)
      10
      • No Work Files.txt (0.0 KB)
      2. Getting Started with This Course
      • 1. Introduction.mp4 (51.0 MB)
      • 1. Introduction.srt (7.5 KB)
      • 2. Learn How to Work with Unicode.mp4 (51.2 MB)
      • 2. Learn How to Work with Unicode.srt (7.8 KB)
      3. Learn to Convert Text To Symbols
      • 1. Introduction.mp4 (45.2 MB)
      • 1. Introduction.srt (7.7 KB)
      • 2. Text To Symbols - Splitting Sentences.mp4 (40.2 MB)
      • 2. Text To Symbols - Splitting Sentences.srt (5.3 KB)
      • 3. Text To Symbols - Filtering Stop Words.mp4 (25.1 MB)
      • 3. Text To Symbols - Filtering Stop Words.srt (3.3 KB)
      4. Python - Going Subsymbolic
      • 1. Introduction.mp4 (108.1 MB)
      • 1. Introduction.srt (15.1 KB)
      • 2. Learn About Word Vectors.mp4 (21.1 MB)
      • 2. Learn About Word Vectors.srt (5.7 KB)
      • 3. Learn About Google Word Vectors.mp4 (50.8 MB)
      • 3. Learn About Google Word Vectors.srt (6.2 KB)
      • 4. Subsymbolic - Learn Word Vectors.mp4 (106.5 MB)
      • 4. Subsymbolic - Learn Word Vectors.srt (13.3 KB)
      5. Learn About the Structure of Text
      • 1. Introduction.mp4 (31.8 MB)
      • 1. Introduction.srt (5.9 KB)
      • 2. Learn and Understand Sentence Head.mp4 (21.1 MB)
      • 2. Learn and Understand Sentence Head.srt (3.7 KB)
      • 3. Learn and Understand Named Entities.mp4 (29.7 MB)
      • 3. Learn and Understand Named Entities.srt (5.2 KB)
      6. Machine Learning - Determining How the Writer Feels
      • 1. Introduction.mp4 (20.7 MB)
      • 1. Introduction.srt (5.6 KB)
      • 2. Machine Learning - Sentiment In VADER.mp4 (65.4 MB)
      • 2. Machine Learning - Sentiment In VADER.srt (8.4 KB)
      7. Machine Learning - Making Decisions
      • 1. Introduction.mp4 (13.7 MB)
      • 1. Introduction.srt (4.5 KB)
      • 2. Classification with TextBlob.mp4 (141.8 MB)
      • 2. Classification with TextBlob.srt (16.0 KB)
      • 3. Classification with scikit-learn.mp4 (94.7 MB)
      • 3. Classification with scikit-learn.srt (11.2 KB)
      8. Machine Learning - Identifying Discussed Topics
      • 1. Introduction.mp4 (13.8 MB)
      • 1. Introduction.srt (4.3 KB)
      • 2. Learn About LDA Gensim.mp4 (95.6 MB)
      • 2. Learn About LDA Gensim.srt (11.6 KB)
      • 3. Learn About LDA pyLDAvis.mp4 (47.9 MB)
      • 3. Learn About LDA pyLDAvis.srt (6.0 KB)
      9. Machine Learning - Toward Machine Reading
      • 1. Introduction.mp4 (29.5 MB)
      • 1. Introduction.srt (5.3 KB)
      • 2. Learn About pyspotlight.mp4 (32.2 MB)
      • 2. Learn About pyspotlight.srt (4.8 KB)
      • 3. Learn About FRED.mp4 (34.5 MB)
      • 3. Learn About FRED.srt (5.1 KB)
      • Bonus Resources.txt (0.3 KB)

Description

AI with Python - Natural Language Processing (NLP) (Updated)



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 26 lectures (1h 52m) | Size: 1.17 GB
Maximize your NLP capabilities while creating amazing NLP projects in Python
What you'll learn:
Core Concepts
Convert Text to Symbols
Developing a Text Classifier
Vector Representation
Learn basic string processing in python
Learn how to tokenize text so it can be processed as symbols
Identify the grammatical parts of a sentence
Understand the capabilities and limitations of NLP

Requirements
Basic Python programming knowledge is helpful but not required

Description
Python is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning.

This course will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization. The course starts with an introduction to NLP. You’ll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you’ll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you’ll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews.



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Udemy - AI with Python - Natural Language Processing (NLP) (Updated)
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