Natural Language Processing

Natural Language Processing (NLP)

taught by Nitin Indurkhya


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Aim of Course:

This online course, “Natural Language Processing,” is designed to give you an introduction to the algorithms, techniques and software used in natural language processing (NLP). Their use will be illustrated by reference to existing applications, particularly speech understanding, information retrieval, machine translation and information extraction. The course will try to make clear both the capabilities and the limitations of these applications.

For real-world applications, NLP draws heavily on work in computational linguistics and artificial intelligence. The course textbook will provide the necessary background in linguistics and computer science for those students who need it. In this course only a portion of the textbook will be covered, however anyone going on to do further studies in NLP will find the textbook a very useful reference.

At the completion of the course, a student should be able to read the description of an NLP application and have an idea of how it is done, what the likely weaknesses are, and often which claims are probably exaggerated. The course also prepares students to do further work in NLP by giving them a good grasp of the basic concepts.

Anticipated learning outcomes:

  • Correctly understand and produce regular expressions
  • Understand and give examples of N-grams and their role in probabilistic language prediction
  • Assign (tag) parts of speech to words in a corpus
  • Parse sentences
  • Use semantic analysis to understand meaning
  • Disambiguate word meanings
This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

WEEK 1: Introduction of NLP and Word-level Analysis

  • Overview of NLP
  • Regular Expressions
  • Morphology
  • N-Grams

WEEK 2: Sentence-level Processing

  • Part-of-Speech Tagging
  • Context-Free Grammars (CFG)
  • Parsing of sentences with CFG
  • Statistical parsing methods

WEEK 3: Semantics

  • Representation of Meaning
  • Semantic Analysis
  • Word Sense Disambiguation

WEEK 4: Applications of NLP

  • Information Retrieval
  • Information Extraction
  • Speech Recognition Systems
  • Machine Translation


Homework in this course consists of short answer questions to test concepts.

In addition to assigned readings, this course also has supplemental readings available online.

Natural Language Processing

Who Should Take This Course:
Analysts, researchers and managers who deal with, or might need to deal with, NLP systems at a variety of levels - needs assessment, design, deployment and operation.
Students should be familiar with probability (e.g. the material covered in Statistics 1 and Statistics 2). Some familiarity with Bayesian statistics (such as that covered in Introduction to Bayesian Statistics) is also helpful, although the text does cover the required Bayesian fundamentals to a limited degree. Keep in mind that this course is an introductory/survey course with a broad brush approach, and, as such, does not get into computational intensity on a comprehensive basis.

Organization of the Course:
Options for Credit and Recognition:
Specializations are an easy way for you to demonstrate mastery of a specific skill in statistics and analytics. This course is part of the Text Mining and Analytics Specialization which gives a deep dive into text mining, natural language processing and sentiment analysis. Requires Python and some familiarity with Bayesian statistics.

Course Text:

The course text is Speech and Language Processing, 2nd Edition, by Daniel Jurafsky, James H. Martin.




July 12, 2019 to August 09, 2019 February 28, 2020 to March 27, 2020

Natural Language Processing


July 12, 2019 to August 09, 2019 February 28, 2020 to March 27, 2020

Course Fee: $549

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We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

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