wendy729 / python_natural_language_processing

This repository consists of a complete guide on natural language processing (NLP) in Python where we'll learn various techniques for implementing NLP including parsing & text processing and understand how to use NLP for text feature engineering.

Python_Natural_Language_Processing

This repository contains tutorials on important topics related to Natural Language Processing (NPL).

No.	Name
01	01_Tokenization_NLP
02	02_Stemming_Lemmatization
03	03_StopWords
04	04_Vocabulary_and_Matching
05	05_POS_Basics
06	06_Named_Entity_Recognition
07	07_Sentence_Segmentation
08	08_Stemming
09	09_BagofWords_N_Gram
10	10_TF_IFD

These are read-only versions. However you can Run ▶ all the codes online by clicking here ➞ 020_Road_Detection

Frequently asked questions ❔

How can I thank you for writing and sharing this tutorial? 🌷

You can and Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this tutorial.

Go here if you aren't here already and click ➞ ✰ Star and ⵖ Fork button in the top right corner. You will be asked to create a GitHub account if you don't already have one.

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How can I read this tutorial without an Internet connection?

1. Go here and click the big green ➞ Code button in the top right of the page, then click ➞ Download ZIP.

   

2. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.

3. Launch ipython notebook from the folder which contains the notebooks. Open each one of them

   Kernel > Restart & Clear Output

This will clear all the outputs and now you can understand each statement and learn interactively.

If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.

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Authors ✍️

I'm Dr. Milaan Parmar and I have written this tutorial. If you think you can add/correct/edit and enhance this tutorial you are most welcome🙏

See github's contributors page for details.

If you have trouble with this tutorial please tell me about it by Create an issue on GitHub and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.

If you like this tutorial, please give it a ⭐ star.

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Licence 📜

You may use this tutorial freely at your own risk. See LICENSE.