Natural language processing (NLP) enables computers to understand, interpret, and generate human language. Common sub-topics include text classification, sentiment analysis, named entity recognition, and machine translation. It applies techniques from Machine Learning and Deep Learning
To study Natural Language Processing (NLP), students usually need to have backgrounds in:
Princeton University
Spring 2023
This course introduces the basics of NLP, including recent deep learning approaches. It covers a wide range of topics, such as language modeling, text classification, machine translation, and question answering.
No concepts data
+ 13 more conceptsStanford University
Winter 2023
CS 224N provides an in-depth introduction to neural networks for NLP, focusing on end-to-end neural models. The course covers topics such as word vectors, recurrent neural networks, and transformer models, among others.
No concepts data
+ 21 more conceptsStanford University
Winter 2023
This course is centered on extracting information from unstructured data in language and social networks using machine learning tools. It covers techniques like sentiment analysis, chatbot development, and social network analysis.
No concepts data
+ 14 more conceptsUniversity of Washington
Winter 2022
This course provides a comprehensive overview of Natural Language Processing (NLP), including core components like text classification, machine translation, and syntax analysis. It offers two project types: implementation problem-solving for CSE 447, and reproducing experiments from recent NLP papers for CSE 517.
No concepts data
+ 16 more conceptsCarnegie Mellon University
Spring 2021
Focused on computational systems for human languages, this course introduces various NLP applications, such as translation and summarization. It encompasses a broad scope, from machine learning to linguistics, with a software engineering perspective.
No concepts data
+ 28 more concepts