Text Processing Course: Stanford Natural Language Processing

Deep Learning Specialization on Coursera

Name: Natural Language Processing

Website: https://www.coursera.org/course/nlp


This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning.

We are offering this course on Natural Language Processing free and online to students worldwide, continuing Stanford’s exciting forays into large scale online instruction. Students have access to screencast lecture videos, are given quiz questions, assignments and exams, receive regular feedback on progress, and can participate in a discussion forum. Those who successfully complete the course will receive a statement of accomplishment. Taught by Professors Jurafsky and Manning, the curriculum draws from Stanford’s courses in Natural Language Processing. You will need a decent internet connection for accessing course materials, but should be able to watch the videos on your smartphone.

About the Instructors

Dan Jurafsky
Stanford University

Dan Jurafsky is Professor of Linguistics and Professor by Courtesy of Computer Science at Stanford University. Dan received his Bachelor’s degree in Linguistics in 1983 and his Ph.D. in Computer Science in 1992, both from the University of California at Berkeley, and also taught at the University of Colorado, Boulder before joining the Stanford faculty in 2004. He is the recipient of a MacArthur Fellowship and has served on a variety of editorial boards, corporate advisory boards, and program committees. Dan’s research extends broadly throughout natural language processing as well as its application to the behavioral and social sciences.

Christopher Manning
Associate Professor
Stanford University

Christopher Manning is an Associate Professor of Computer Science and Linguistics at Stanford University. Chris received a Bachelors degree and University Medal from the Australian National University and a Ph.D. from Stanford in 1994, both in Linguistics. Chris taught at Carnegie Mellon University and The University of Sydney before joining the Stanford faculty in 1999. He is a Fellow of the American Association for Artificial Intelligence and of the Association for Computational Linguistics, and is one of the most cited authors in natural language processing, for his research on a broad range of statistical natural language topics from tagging and parsing to grammar induction and text understanding.

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