[ FreeCourseWeb ] Udemy - Master Natural Language Processing (NLP) with Python

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[ FreeCourseWeb ] Udemy - Master Natural Language Processing (NLP) with Python (Size: 739.2 MB)
  1. Ambiguities in NLP-en_US.srt 5.6 KB
  1. Ambiguities in NLP.mp4 17.4 MB
  1. Case Study Sentiment Analysis & Word Cloud-en_US.srt 11.1 KB
  1. Case Study Sentiment Analysis & Word Cloud.mp4 54.1 MB
  1. Introduction to Colab Google Cloud Development Environment-en_US.srt 8.3 KB
  1. Introduction to Colab Google Cloud Development Environment.mp4 25.9 MB
  1. Introduction-en_US.srt 1.4 KB
  1. Introduction.mp4 11.3 MB
  1. Key concepts in NLP Sentence Segmentation-en_US.srt 2.7 KB
  1. Key concepts in NLP Sentence Segmentation.mp4 13.2 MB
  1. NLTK and NLP in action-en_US.srt 7.3 KB
  1. NLTK and NLP in action.mp4 31.8 MB
  10. Tuple-en_US.srt 5.3 KB
  10. Tuple.mp4 19.8 MB
  11. Set-en_US.srt 4.8 KB
  11. Set.mp4 19.6 MB
  12. Dictionary-en_US.srt 2.8 KB
  12. Dictionary.mp4 13 MB
  13. Getting Started with NumPy-en_US.srt 8.7 KB
  13. Getting Started with NumPy.mp4 28.5 MB
  14. NumPy Shape in Arrays-en_US.srt 4 KB
  14. NumPy Shape in Arrays.mp4 12.7 MB
  15. NumPy Iterating Arrays-en_US.srt 1.5 KB
  15. NumPy Iterating Arrays.mp4 5.7 MB
  16. NumPy Joining Arrays-en_US.srt 2.8 KB
  16. NumPy Joining Arrays.mp4 13.4 MB
  17. NumPy Splitting Arrays-en_US.srt 1.7 KB
  17. NumPy Splitting Arrays.mp4 6.1 MB
  18. NumPy Searching and Sorting Arrays-en_US.srt 2.7 KB
  18. NumPy Searching and Sorting Arrays.mp4 10.3 MB
  19. Getting Started with Pandas-en_US.srt 7.3 KB
  19. Getting Started with Pandas.mp4 34.3 MB
  2. Case Study Speech to Text deployment in a call center-en_US.srt 9 KB
  2. Case Study Speech to Text deployment in a call center.mp4 37.3 MB
  2. Getting Started with Python-en_US.srt 4.6 KB
  2. Getting Started with Python.mp4 13.7 MB
  2. Key concepts in NLP Word Tokenization-en_US.srt 1.8 KB
  2. Key concepts in NLP Word Tokenization.mp4 7.4 MB
  2. Noise removal-en_US.srt 1.7 KB
  2. Noise removal.mp4 9.8 MB
  20. Pandas Dataframe-en_US.srt 6.4 KB
  20. Pandas Dataframe.mp4 28.1 MB
  21. Pandas Descriptive Statistics-en_US.srt 3.5 KB
  21. Pandas Descriptive Statistics.mp4 10.8 MB
  22. Pandas Sorting, Slicing, Flipping, Grouping Data-en_US.srt 7.2 KB
  22. Pandas Sorting, Slicing, Flipping, Grouping Data.mp4 22.7 MB
  23. Data Visualization using Matplotlib-en_US.srt 8.4 KB
  23. Data Visualization using Matplotlib.mp4 33 MB
  3. Key concepts in NLP Stemming-en_US.srt 1.8 KB
  3. Key concepts in NLP Stemming.mp4 9 MB
  3. Spacy-en_US.srt 2.5 KB
  3. Spacy.mp4 11.2 MB
  3. Text Summarization-en_US.srt 3.9 KB
  3. Text Summarization.mp4 24.4 MB
  3. Variables-en_US.srt 7.4 KB
  3. Variables.mp4 24.6 MB
  4. Case Study 4 Spam Classification Using Machine Learning-en_US.srt 6.6 KB
  4. Case Study 4 Spam Classification Using Machine Learning.mp4 29.8 MB
  4. Flash Text-en_US.srt 1.4 KB
  4. Flash Text.mp4 10.1 MB
  4. Key concepts in NLP Lemmatization-en_US.srt 1.6 KB
  4. Key concepts in NLP Lemmatization.mp4 6.5 MB
  4. Operators-en_US.srt 5.4 KB
  4. Operators.mp4 18.8 MB
  5. Conditions-en_US.srt 6.3 KB
  5. Conditions.mp4 18.3 MB
  5. Key concepts in NLP Stop Words-en_US.srt 2 KB
  5. Key concepts in NLP Stop Words.mp4 6.7 MB
  5. Named Entity Recognition (NER)-en_US.srt 2.5 KB
  5. Named Entity Recognition (NER).mp4 13.3 MB
  6. Key concepts in NLP Dependency Parsing-en_US.srt 1.3 KB
  6. Key concepts in NLP Dependency Parsing.mp4 4.6 MB
  6. Loops-en_US.srt 7.3 KB
  6. Loops.mp4 19.9 MB
  7. Functions-en_US.srt 5.3 KB
  7. Functions.mp4 15.3 MB
  7. Key concepts in NLP Parts of Speech-en_US.srt 3 KB
  7. Key concepts in NLP Parts of Speech.mp4 11.2 MB
  8. Arrays-en_US.srt 4 KB
  8. Arrays.mp4 20.1 MB
  9. List-en_US.srt 4.5 KB
  9. List.mp4 15.4 MB
  Bonus Resources.txt 307.2 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 84 total files

Description


Master Natural Language Processing (NLP) with Python

MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 739 MB | Duration: 2h 43m
What you'll learn
You will gain insights on what Natural Language Processing(NLP) is, its Applications & Challenges
You will learn Sentence Segmentation, Word Tokenization, Stemming, Lemmatization, Parsing, POS & Ambiguities in NLP
You will learn to execute using Machine Learning, NLTK & Spacey
You will learn to work with Text Files with Python
You will utilize Regular Expressions for pattern searching in text
You will use Part of Speech Tagging to automatically process raw text files
You will visualize POS and NER with Spacy
You will understand Vocabulary Matching with Spacy
You will use NLTK for Sentiment Analysis

Requirements
None. (Python is covered extensively in the course)
Description
Natural Language Processing (NLP) is a very interesting field associated with AI and is at the forefront of many useful applications like a chatbot. Knowledge of NLP is considered a necessity for those pursuing a career in AI. This course covers both the theory as well as the applications of NLP. Case studies are explained along with a walkthrough of the codes for a better understanding of the subject.

NLP is a subfield of computer science and artificial intelligence concerned with interactions between computers and human (natural) languages. It is used to apply machine learning algorithms to text and speech.

For example, we can use NLP to create systems like speech recognition, document summarization, machine translation, spam detection, named entity recognition, question answering, autocomplete, predictive typing and so on.

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