# Overview

Natural Language Processing (NLP) is a dynamic field within Artificial Intelligence focused on developing computational models to understand, interpret, and generate human language. As NLP technologies become increasingly embedded in our daily lives, understanding its fundamentals  is crucial for both leveraging its potential and enhancing our interaction with language-based systems.

This course is designed to build a robust foundation in the core principles of modern NLP. We begin with text processing techniques that show how to manipulate textual data for model development. We then progress to language models, enabling computational models to comprehend and generate human language, and vector space models that convert textual data into machine-readable formats. Advanced topics include distributional semantics for creating context-aware word embeddings, and contextual encoding for analyzing word relationships within their surrounding text.

The latter part of the course focuses on practical application through team projects. Students will have the opportunity to work with cutting-edge NLP technologies, such as large language models, to develop real-world applications. This hands-on approach encourages creativity and innovation, with students proposing their own ideas and presenting demonstrations to their peers.

Learning assessment combines concept quizzes to reinforce theoretical understanding with hands-on programming assignments that develop practical implementation skills. By the conclusion of the course, students will have gained the knowledge and skills to navigate and contribute to the rapidly evolving field of NLP.

### Prerequisites

* Introduction to Python Programming
* Introduction to Machine Learning

## Sections

* [Syllabus](/nlp-essentials/overview/syllabus.md)
* [Schedule](/nlp-essentials/overview/schedule.md)
* [Development Environment](/nlp-essentials/overview/development-environment.md)
* [Homework](/nlp-essentials/overview/homework.md)


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