Text Processing using NLTK in Python by Packt Publishing
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Text Processing Using NLTK in Python [Video]

This is the code repository for Text Processing Using NLTK in Python [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

Natural Language Processing (NLP) is a feature of Artificial Intelligence concerned with the interactions between computers and human (natural) languages. This course includes unique videos that will teach you various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task. In this course, you will learn what WordNet is and explore its features and usage. It will teach how to extract raw text from web sources and introduce some critical pre-processing steps. You will also get familiarized with the concept of pattern matching as a way to do text analysis. By the end of the course, you will be confident & have covered various solutions, covering natural language understanding, Natural Language Processing, and syntactic analysis.

What You Will Learn

  • Build solutions such as text similarity, summarization, sentiment analysis and anaphora resolution to get up to speed with new trends in NLP.
  • Write your own POS taggers and grammars so that any syntactic analyses can be performed easily.
  • Use the inbuilt chunker and create your own chunker to evaluate trained models.
  • Create your own named entities using dictionaries to use inbuilt text classification algorithms.
  • Combine various lessons and create applicable solutions that can be easily plugged into any of your real-life application problems.

Instructions and Navigation

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:
This video course is ideal for Data Scientists, Data Analysts, and Data Science professionals who want to upgrade their existing skills to learn text analytics using NLP. Some basic knowledge of Python is recommended.

Technical Requirements

This course has the following software requirements:

  • Python 3.x or higher running on any Windows or Unix
  • Processor of 2.0 GHz or higher
  • Minimum 4GB RAM

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