Text Mining and Analytics


Text Mining and Analytics

This course will cover the major techniques for mining and analysing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.

Target Audience

  • This course is ideal for individuals having interest in Artificial Intelligence, Machine Learning, or Deep Learning. In other words, this course is meaningful for Python Developers, Robotics Engineer, and Fresh Graduates.


  • As such, there are no technical prerequisites for this course. However, knowledge of python and mathematical aptitude will be beneficial

Course Objectives

  • This course describes a range of business opportunities and solutions centered around the use of text and it also identify sources of competitive intelligence, in text, and provide solutions for parsing and storing incoming knowledge. It uses real-world case studies, the course provides examples of the most useful statistical and machine learning techniques for handling text, semantic, and social data. We then describe how and what you can infer from the data, and discuss useful techniques for visualizing and communicating the results to decision-makers.



Course Curriculum

Section 1 : Text Mining Techniques

  • Introduction to Text Mining and Analytics
  • Natural Language Content Analysis
  • Text Representation
  • Word Association Mining and Analysis
  • Paradigmatic Relation Discovery
  • Syntagmatic Relation Discovery
  • Topic Mining and Analysis
  • Probabilistic Topic Models
  • Latent Dirichlet Allocation
  • Text Clustering
  • Text Categorization
  • Opinion Mining and Sentiment Analysis
  • Text-Based Prediction
  • Contextual Text Mining