Summary  
This chapter introduces the programming concept of natural language processing, covering techniques for enabling code to understand, interpret, and generate human language.

General domain of usage  
Chatbots

In order to feel confident and successfully complete this course, we strongly recommend you **complete the following courses** beforehand (just click on them to start):

**Natural Language Processing (NLP)** is a field at the intersection of AI, linguistics, and computer science. Its goal is to enable computers to understand, interpret, and generate **human language**, powering technologies like search engines, voice assistants, translation systems, and chatbots.

NLP appears in many daily applications:

* **Search engines** interpret query intent;
* **Virtual assistants** understand voice commands;
* **Translation tools** provide real-time language conversion;
* **Sentiment analysis** detects opinions and emotions;
* **Chatbots** deliver instant automated support.


Explore the fundamentals of Natural Language Processing (NLP) by learning essential text preprocessing techniques and methods for representing text data. Gain practical experience with the tools used to clean, analyze, and interpret textual information. Develop the skills needed to transform raw language into structured insights, laying a strong foundation for advanced applications in artificial intelligence and machine learning.

Dive into the fundamentals of text preprocessing to prepare raw text for analysis. Learn how to tokenize text, filter out stop words, and customize tokenization with regular expressions.

Discover how words can be reduced to their base forms using stemming and lemmatization. Master part-of-speech tagging to enrich text with grammatical context and apply POS-aware lemmatization.

Learn how text can be represented with numbers using vector space models. Get hands-on experience by implementing and customizing two popular vector space models: bag of words and TF-IDF.

Gain a solid understanding of word embeddings and how they capture semantic meaning. Explore CBoW and Skip-gram architectures used in Word2Vec, and implement them on your own.

NLP and Its Applications

Applications of NLP