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

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Udemy - AI with Python - Natural Language Processing (NLP) (Updated) (Size: 1.2 GB)
  1 Unicode Python 2.ipynb 5.6 KB
  1. Course Summary.mp4 12.3 MB
  1. Course Summary.srt 4 KB
  1. Introduction.mp4 29.5 MB
  1. Introduction.srt 5.3 KB
  2 Unicode Python 3.ipynb 1.7 KB
  2. Classification with TextBlob.mp4 141.8 MB
  2. Classification with TextBlob.srt 16 KB
  2. Course Material & Source Code.html 0 B
  2. Learn About LDA Gensim.mp4 95.6 MB
  2. Learn About LDA Gensim.srt 11.6 KB
  2. Learn About Word Vectors.mp4 21.1 MB
  2. Learn About Word Vectors.srt 5.7 KB
  2. Learn About pyspotlight.mp4 32.2 MB
  2. Learn About pyspotlight.srt 4.8 KB
  2. Learn How to Work with Unicode.mp4 51.2 MB
  2. Learn How to Work with Unicode.srt 7.8 KB
  2. Learn and Understand Sentence Head.mp4 21.1 MB
  2. Learn and Understand Sentence Head.srt 3.7 KB
  2. Machine Learning - Sentiment In VADER.mp4 65.4 MB
  2. Machine Learning - Sentiment In VADER.srt 8.4 KB
  2. Text To Symbols - Splitting Sentences.mp4 40.2 MB
  2. Text To Symbols - Splitting Sentences.srt 5.3 KB
  3. Classification with scikit-learn.mp4 94.7 MB
  3. Classification with scikit-learn.srt 11.2 KB
  3. Learn About FRED.mp4 34.5 MB
  3. Learn About FRED.srt 5.1 KB
  3. Learn About Google Word Vectors.mp4 50.8 MB
  3. Learn About Google Word Vectors.srt 6.2 KB
  3. Learn About LDA pyLDAvis.mp4 47.9 MB
  3. Learn About LDA pyLDAvis.srt 6 KB
  3. Learn and Understand Named Entities.mp4 29.7 MB
  3. Learn and Understand Named Entities.srt 5.2 KB
  3. Text To Symbols - Filtering Stop Words.mp4 25.1 MB
  3. Text To Symbols - Filtering Stop Words.srt 3.3 KB
  4. Subsymbolic - Learn Word Vectors.mp4 106.5 MB
  4. Subsymbolic - Learn Word Vectors.srt 13.3 KB
  Bonus Resources.txt 307.2 B
  Dependency Parsing.ipynb 5.6 KB
  Entity Recognition.ipynb 4.5 KB
  FRED.ipynb 2.8 KB
  Get Bonus Downloads Here.url 204.8 B
  Head of a Sentence.ipynb 2.9 KB
  LDA_with_gensim.ipynb 21.7 KB
  No Work Files.txt 0 B
  Sentiment_VADER.ipynb 4.2 KB
  Text_Classification_With_TextBlob.ipynb 14.5 KB
  Text_Classification_with_scikit-learn.ipynb 88.6 KB
  Train Word Vectors2.ipynb 23.5 KB
  Use Google Word Vectors.ipynb 29.4 KB
  our_textblob_classifiers.py 20.1 KB
  pyLDAvis.ipynb 155.4 KB
  pyspotlight.ipynb 3.1 KB
  sentences NLTK spaCy.ipynb 3.4 KB
  stop words in NLTK.ipynb 6.3 KB
  string processing.ipynb 7.1 KB
  tf-idf Gensim.ipynb 11.2 KB
  tokens NLTK spaCy.ipynb 7.9 KB
  ▲ 75 total files

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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