Text Mining

Peter Bruce, President and Founder of Statistics.com, says, as I read about the frenzy of interest in mining Twitter data, I think of the 1949 Gold Rush, which boosted the settler population of California from under 1,000 to over 100,000 in two years.  Most miners made little money, but suppliers of tools and owners of technology did quite well.

"Text Mining" will introduce 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.

Learn about text mining techniques in Nitin Indurkhya's online course, "Text Mining," at statistics.com. For more details please visit http://www.statistics.com/textmining.

Who can 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.).

Course Program:

Course outline: The course is structured as follows

 

SESSION 1: Introduction and Data preparation

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

SESSION 2: Predictive Models for Text

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

SESSION 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

SESSION 4: Information Extraction

• Goals of information extraction
• Finding patterns and entities
• Entity Extraction: The Maximum Entropy method
• Template filling
• Applications

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.

Software:

Python is used in the course.

Instructor Dr. Nitin Indurkhya, co-author of "Text Mining" (Springer) and co-editor of the "Handbook of Natural Language Processing" (CRC), was also Principal Research Scientist at eBay and Professor at the School of Computer Science and Engineering, University of New South Wales (Australia), as well as the founder and president of Data-Miner Pty Ltd, an Australian company engaged in data-mining consulting and education.

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

We, the Center for eLearning and Training (C-eLT), Pune, partner with Statistics.com and offer these courses to Indian participants at special prices payable in INR.

For India Registration and pricing, please visit us at www.india.statistics.com.

Email: info@c-elt.com

Call: 020 6680 0300 / 322

Websites:

www.statistics.com/india  

www.c-elt.com