Add to Buffet

Save course to Your Buffet - Get notified, Track Progress, Plan Future Learning.
117 People Have this course in their Buffet

User Rating

Course Reviewed by 1 Users
Overall Rating:
How much did you Learn:
How much did you Enjoy:
User Dificulty Level:
Too Easy : %
Easy : %
Moderate : %
Hard : 100%
Too Hard : %

Web Intelligence and Big Data

Course Description:

The past decade has witnessed the successful of application of many AI techniques used at `web-scale’, on what are popularly referred to as big data platforms based on the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-SQL databases and stream computing engines. Online advertising, machine translation, natural language understanding, sentiment mining, personalized medicine, and national security are some examples of such AI-based web-intelligence applications that are already in the public eye. Others, though less apparent, impact the operations of large enterprises from sales and marketing to manufacturing and supply chains. In this course we explore some such applications, the AI/statistical techniques that make them possible, along with parallel implementations using map-reduce and related platforms.

  • Instructor(s) Gautam Shroff
  • University
  • Provider
  • Start Date 20/Apr/2014
  • Duration 10 weeks
  • Main Language English
Did you find any errors in this course listing ? Help us improve and we would be eternally grateful

Related Courses

Other Computer Science Courses

Course Reviews

  • No Comments Yet! Be the first one to comment.
  • amanda_dsouza
    Amanda Completed Course Jul 03 2013
    Know your math This course was not quite what I expected. You have to know your math & probability well to understand the concepts. I wish they'd focused more on the application of big data techniques. I was also hoping for more of Hadoop/Map Reduce examples; there was only one section dedicated to that.
    I do think it did well in introducing a lot of different web intelligence/big data analysis techniques available currently & terms & concepts involved.
    In summary, this is sort-of an introductory course with a lot of emphasis on the math.