Natural Language Processing

Graduate Certificate

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Quick Facts

Credits Required: 9

Cost Per Credit: $800.00

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College of Social And Behavioral Sciences

Program Details

Earn a Graduate Certificate in Natural Language Processing and enter the rapidly growing field of Natural Language Processing (NLP) with expertise in related technologies and applications. This program integrates foundational skills of data science and machine learning so you are prepared to work in governmental agencies, nonprofit organizations and more.

Natural Language Processing is the process of teaching computers to use language by extracting knowledge from text and then using that knowledge in meaningful ways such as text summarization, speech recognition, text to speech, and related areas. The certificate covers core algorithms and methods for NLP, such as training in statistical NLP, computational linguistics, speech technology, text retrieval and/or neural networks.

Learning tools for critical tasks such as machine translation, speech recognition, speech synthesis, grammar checking and text mining qualifies you for a range of positions in this in-demand field.

The Natural Language Processing Graduate Certificate includes many courses that are also part of the MS in Human Language Technology. Completing this certificate will prepare you well to enter the MS in Human Language Technology program, where you'll be equipped with even more tools and skills for work in natural language processing. Learn more about the MS in HLT. This certificate also provides you with credits and experience that are directly transferable into the MS in Data Science. Learn more about the MS in Data Science.

View detailed program information

Preferred requirements for admission to the certificate program include a bachelor’s degree in computer science, information science, linguistics or related disciplines and experience in coding and statistics.

*Residents of some U.S. Territories may not be eligible. Please see our Eligibility & State Authorization page for more information.

Courses

The program consists of two required courses plus an elective chosen from a slate of five possibilities:

(Required) This course introduces the key concepts underlying statistical natural language processing. You will learn a variety of techniques for the computational modeling of natural language, including n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation-Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models.

(Required) This course focuses on statistical approaches to pattern classification and applications of natural language processing to real-world problems.

This course provides a hands-on project-based approach to particular problems and issues in computational linguistics.

Topics include speech synthesis, speech recognition, and other speech technologies. This course gives students background for a career in the speech technology industry. Graduate students will do extra readings, extra assignments, and have an extra presentation. Their final project must constitute original work in speech technology.

In this course you will learn how to train and optimize feed forward, convolutional, and recurrent neural networks for tasks such as text classification, image recognition, and game playing.

Gain the fundamental knowledge necessary to build these systems, such as web crawling, index construction and compression, Boolean, vector-based, and probabilistic retrieval models, text classification and clustering, link analysis algorithms such as PageRank, and computational advertising. You will also complete one programming project, in which you will construct one complex application that combines multiple algorithms into a system that solves real-world problems.

Learn important algorithms useful for natural language processing (NLP), including distributional similarity algorithms such as word embeddings, recurrent and recursive neural networks (NN), probabilistic graphical models useful for sequence prediction, and parsing algorithms such as shift-reduce. This course will focus on the algorithms that underlie NLP, rather than the application of NLP to various problem domains.