Text Mining using Python

Text Mining using Python

taught by Anurag Bhardwaj

 

 
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Aim of Course:

In this online course, “Text Mining using Python,” 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 using Python

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:
Organization of the Course:
Options for Credit and Recognition:
Specialization:
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 required text is Fundamentals of Predictive Text Mining (Springer, 2015) by Weiss, Indurkhya and Zhang.  Be sure to purchase the 2015 edition. It may be purchased here.

Software:
Assignments can be done in Python, and instructor can assist with Python.  Some familiarity with Python is needed.  An optional alternative is Java-based software available from the instructor's website  http://www.data-miner.com/software.html .
Instructor(s):

Dates:

June 07, 2019 to July 05, 2019 January 31, 2020 to February 28, 2020

Text Mining using Python

Instructor(s):

Dates:
June 07, 2019 to July 05, 2019 January 31, 2020 to February 28, 2020

Course Fee: $549

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

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

Group rates: Click here to get information on group rates. 

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