Natural Language Processing (NLP)
taught by Nitin Indurkhya
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.
WEEK 1: Introduction of NLP and Word-level Analysis
- Overview of NLP
- Regular Expressions
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
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:
This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.
At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.
About 15 hours per week, at times of your choosing.
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
- No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
- Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
- CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). For those successfully completing the course, CEU's and a record of course completion will be issued by The Institute, upon request.
- Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses
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. Take any three of the four Statistics.com courses on this topic (this course, plus the courses listed to the right under "related courses," not including conferences). For savings, use the promo code "text-specialization" and register for all three courses at once for $1197 ($399 per course, not combinable with other tuition savings). If you register for all four, you'll still receive the discounted rate.
The course text is Speech and Language Processing, 2nd Edition, by Daniel Jurafsky, James H. Martin.
PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.
To be scheduled.
Natural Language Processing
To be scheduled.
Course Fee: $549
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