Machine Learning Times
Machine Learning Times
Visualizing Decision Trees with Pybaobabdt
 Originally published in Towards Data Science, Dec 14, 2021....
Correspondence Analysis: From Raw Data to Visualizing Relationships
 Isn’t it satisfying to find a tool that makes...
Podcast: Four Things the Machine Learning Industry Must Learn from Self-Driving Cars
    Welcome to the next episode of The Machine...
A Refresher on Continuous Versus Discrete Input Variables
 How many times have I heard that the most...

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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