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Text Mining

Instructor(s):

Dates:

February 06, 2015 to March 06, 2015 June 05, 2015 to July 03, 2015 February 05, 2016 to March 04, 2016 June 10, 2016 to July 08, 2016 February 03, 2017 to March 03, 2017 June 09, 2017 to July 07, 2017 February 02, 2018 to March 02, 2018 June 08, 2018 to July 06, 2018

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Text Mining

taught by Anurag Bhardwaj and Nitin Indurkhya

Aim of Course:

In this online course, “Text Mining,” you will be introduced to the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text. This course will discuss these standard techniques, and will devote considerable attention to the data preparation and handling methods that are required to transform unstructured text into a form in which it can be mined.  After completing this course students will be able to:

• Perform tokenization and create dictionaries to prepare text for classification tasks
• Create numerical vectors from text data
• Build classifiers with decision trees, Naive Bayes and linear models, using training and validation data
• Perform "tagging" of text data
• Cluster documents using the k-means algorithm
• Generate predicted Twitter hashtags for text data

This course may be taken individually (one-off) or as part of a certificate program.

Course Program:

WEEK 1: Introduction and Data Preparation

  • Overview of text mining
  • Tokenization
  • Dictionary creation
  • Vector generation for prediction
  • Feature generation and selection
  • Parsing

WEEK 2: Predictive Models for Text

  • Document classification
  • Document similarity and nearest-neighbor
  • Decision rules
  • Probabilistic models
  • Linear models
  • Performance evaluation
  • Applications

WEEK 3: Retrieval and Clustering of Documents

  • Measuring similarity for retrieval
  • Web-based document search and link analysis
  • Document matching
  • Clustering by similarity
  • k-means clustering
  • Hierarchical clustering
  • The EM algorithm for clustering
  • Evaluation of clustering

WEEK 4: Information Extraction

  • Goals of information extraction
  • Finding patterns and entities
  • Entity Extraction: The Maximum Entropy method
  • Extraction from web sources


HOMEWORK:

Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

In addition to assigned readings, this course also has a get started guide, and supplemental readings available online.

Text Mining

Instructor(s):

Dates:
February 06, 2015 to March 06, 2015 June 05, 2015 to July 03, 2015 February 05, 2016 to March 04, 2016 June 10, 2016 to July 08, 2016 February 03, 2017 to March 03, 2017 June 09, 2017 to July 07, 2017 February 02, 2018 to March 02, 2018 June 08, 2018 to July 06, 2018

Course Fee: $549

Do you meet course prerequisites? What about book & software? (Click here to learn more)

Tuition Savings:  When you register online for 3 or more courses, $200 is automatically deducted from the total tuition. (This offer cannot be combined and is only applicable to courses of 3 weeks or longer.)

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Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

Text Mining

taught by Anurag Bhardwaj and Nitin Indurkhya

Who Should Take This Course:

IT professionals, web marketing analysts, data mining and statistical consultants. In general: analysts and researchers who need to pilot, implement or analyze data mining methods aimed at data containing unstructured text (forms, surveys, etc.).

Level:

Intermediate

Prerequisite:
These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.
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.

Time Requirement: about 15 hours per week, at times of  your choosing.


Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. 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.
  3. 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, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

The required text is Fundamentals of Predictive Text Mining by Weiss, Indurkhya and Zhang.  It may be purchased here

Software:

Java-based software is available with the book. Python can optionally be used for doing assignments.


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