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Last updated: Feb 28, 2022

Text Preprocessing

It is always necessary to process any dataset or text source before working on it. Creating a text resource so that it can be further analysed and predictions can be made. This section discusses operations such as spell correction, smoothing, tokenization, stemming, and so on.
N-Gram Modelling
This blog will focus on one of the simplest machine learning models called the N-Gram model and its implementation. Let's begin.
Naive Bayes and Laplace Smoothing EASY
This article explains how Naive Bayes and Laplace Smoothing can be integrated to build a better text classifier and how it will help to tackle the zero probability problem.
Computational Morphology
This blog will discuss the morphological information that helps us capture the data and the different linguistic terms involved.
Tokenization in NLP EASY
In this article, you will understand what is Tokenisation in NLP
Stemming in NLP EASY
In this article, you will understand what is Stemming in NLP
Chunking in NLP (Natural Language Processing) EASY
Learn about chunking in NLP (Natural Language Processing), the need of chunking, types of chunking and its implementation, chunking in python, etc.
Lemmatization with TextBlob EASY
This article discusses the theoretical knowledge about Lemmatization with TextBlob.
Bag of Words in NLP EASY
This article gives a detailed overview of the Bag of Words in NLP.
Author Komal
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