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  Hope you are following up with us and by now you have become familiar with Jupyter Notebook , the basics of NLTK and Python functions .  Now, we will explore some more complex topics in NLTK, starting with statistics we will go through the loops and then learn how to apply conditions to the texts.  From the previous article, we know how to find the size of text, and how to access tokens based on indexes. But how to find out which tokens are highly repeated in the text or are there any specific tokens that the author has repeated many times to focus or emphasize on a particular topic.  Yes, this is very simple and can be done easily by using frequency distribution. Frequency Distribution can be understood simply as counting the repetition of different tokens in a text. Eg. In this paragraph, we have tokens ' frequency ', ' distribution ', and ' can ' present twice while 'understood', 'counting', 'repetition', 'using' etc have ju

  Hope you are following us and have installed Python and Anaconda in your systems, if not please refer here  and install it before proceeding further. If you have some system restrictions, then you can log in to Google Colab for free and start working there. It is very similar to Jupyter notebooks , which we will be using throughout our training.  Note:- You can download all the notebooks used in this example here Installations The first step is to install the NLTK library and the NLTK data.  1. Install NLTK using pip command            pip install nltk installing nltk Since it is already installed in my system, it's showing "requirement already satisfied".  Instead of using Jupyter Notebook we can also create a virtual env in our system and follow these steps in conda/ python prompt.  2. Download NLTK data          nltk.download() nltk download This will open a new window NLTK Downloader as shown  It basically contains all the data and other packages for nltk, so we wi

Welcome back to Part 2 of our Natural Language Processing series . As we told you in the beginning these sessions are going to be a mix of both theoretical and practical, so the first thing we need to do is to set our machines for NLP and learn about various libraries that Python has to offer for NLP. If you are new to NLP, then go ahead to Part 1 Introduction to NLP - Getting Started  and learn about the basics of Natural Language Processing, key terminologies and why we need NLP.  Prerequisites 1. Python - 3.7 and above 2. Anaconda or Jupyter Notebook Libraries for NLP Python being an open-source programming language offers a wide range of libraries that can be used for Natural Language Processing(NLP). Here is the list of libraries present in Python for NLP.  1.  Natural Language Toolkit (NLTK) :-     The most common library in Python for NLP is NLTK (Natural Language Toolkit), as it supports a wide range of  languages. Not only this, being an open source it is freely available to s

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