Natural-Language-Processing

What is Natural Language Processing

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that deals with the interactions between computers and human languages. It involves teaching computers to understand, interpret, and generate human language using machine learning algorithms.

The goal of NLP is to enable machines to understand and interpret human language, and to produce language that humans can understand. This involves various tasks such as natural language understanding, natural language generation, machine translation, sentiment analysis, speech recognition, and text summarization, among others.

1.Regular Expression

First Part Covered in my Repository is Regular Expression. Regular expressions are a powerful tool used in natural language processing (NLP) for pattern matching and text processing. They are a sequence of characters that define a search pattern, and are used to identify and extract specific pieces of information from a text document.

For Example

2. Text_preprocessing or processing data with nltk and spacy

Text preprocessing is a crucial step in natural language processing (NLP) that involves cleaning and transforming raw text data into a format that can be analyzed by machine learning models. There are many tools and libraries available for text preprocessing, but two of the most popular are the Natural Language Toolkit (NLTK) and spaCy.

3. Feature Engineering

Feature engineering is an important step in natural language processing (NLP) that involves selecting and transforming the raw text data into a set of features that can be used as input to a machine learning algorithm. The goal of feature engineering is to extract meaningful information from the text data that can be used to build a predictive model

There are some common techniques used for feature engineering in NLP:

• Bag of Words
• Named Entity Recognition (NER)
• Part-of-Speech (POS) Tagging
• Word Embeddings:
• Tf-idf

for more info visit this repository in detail