Summary  
This chapter covers using regular expressions to define custom tokenization patterns with NLTK’s RegexpTokenizer, enabling flexible splitting of text into tokens.

General domain of usage  
Natural language processing

**`RegexpTokenizer`** is a class in **NLTK** designed for tokenizing text data with the use of **regular expressions**. These expressions are powerful **patterns** capable of matching specific sequences in text, like words or punctuation marks. 

The **`RegexpTokenizer`** is particularly advantageous for scenarios demanding **customized tokenization**.

In this project, we will be utilizing the capabilities of the Natural Language Toolkit (NLTK), a versatile and comprehensive library in Python designed for working with human language data. Our focus will encompass several core areas of natural language processing: tokenization, stemming, tagging and parsing. These NLTK features will form the backbone of our text processing and analysis tasks, making it an essential tool in our project for handling and extracting meaningful insights from language data.

In this project, we will be utilizing the capabilities of the Natural Language Toolkit (NLTK), a versatile and comprehensive library in Python designed for working with human language data.

Identifying the Most Frequent Words in Text

Regexp Tokenizer

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