James Kobielus of Info World recently shared his thoughts on the best definition for machine learning. He writes, “Increasingly, the term ‘machine learning’ is… beginning to acquire a catch-all status. Or, at the very least, machine learning has become a convenient handle that today’s data scientists use to refer to the wide range of leading-edge techniques for automating knowledge and pattern discovery from fresh data, much of it unstructured. People’s working definitions of machine learning seem to be creeping into broader, vaguer territory. That’s my impression from reading the recent article “Learning and Teaching Machine Learning: A Personal Journey.” In it, author Joseph R. Barr of San Diego State University and True Bearing Analytics discusses both the history of machine learning and his own education in the topic. He states that ‘it’s safe to regard machine learning, data mining, predictive analysis, and advanced analytics as more or less synonymous’.”


Kobielus goes on, “I’m not sure that lumping machine learning with all of these other techniques makes sense. As noted above, machine learning primarily applies to unstructured data, whereas data mining is specific to structured data sets. Also, machine learning, like data mining, is principally concerned with finding diverse patterns in historical data, whereas predictive analysis focuses specifically on finding those predictive patterns that can be tested empirically through gathering of fresh data in the future. And whereas machine learning, data mining, and predictive analysis are all narrowly scoped, advanced analytics is a broader scope that includes them all.”


Read more here.


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