Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
How to Apply Machine Learning to Business Problems
 Originally published in Emerj, April 25, 2020. It’s easy...
Guidebook to the Future of Data Science: How to Navigate the Increasingly Symbiotic Dynamic Between Executives and Universities
 Book Review of Closing the Analytics Talent Gap: An...
Guilty or Not Guilty: Weight of Evidence
 You have been invited to serve as a juror...
How Machine Learning Works for Social Good
  Originally published in KDnuggets, Nov 2020. This article...
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Presently, text mining is in a loosely organized set of competing technologies with no clear dominance among them. This book chapter organizes text analytics methods as seven complementary practice areas, showing how to select amongst them for your objectives.

We can relate these technologies to seven different practice areas in text mining that are covered in the chapters in this book. In summary, this book is strongest in the practice area of document classification, solid in concept extraction and document clustering, reasonably useful on web mining, light on information extraction and natural
language processing, and almost silent on the (most popular) practice area of search and information retrieval.

Download your Free Book Chapter on Text Mining here

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