Originally published in Data Science Central, Dec 4, 2017
For the latest techniques in and revealing case studies about machine learning and predictive analytics, attend Predictive Analytics World for Business Las Vegas, June 3-7, 2018.
As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives. How about ‘Big Data’? Terrible. It’s not about just size although if you asked most non-DS practitioners that’s what they’d say. Or how about ‘Data Scientist’. Nope. Can’t really agree on that one either.
Now we come to ‘Machine Learning’. If you asked 95 out of 100 data scientists, specifically those who are not doing deep learning they would unanimously agree that this definition hasn’t changed over at least the last 15 years:
The application of any computer-enabled algorithm that can be applied against a data set to find a pattern in the data. This encompasses basically all types of data science algorithms, supervised, unsupervised, segmentation, classification, and regression including deep learning.
Increasingly though there are more and more articles written that hijack this term to mean only deep learning.
It’s natural that the most press is given to the newest and most exciting frontier developments but this is an unnecessary source of confusion. Deep learning specialists have variously argued that machine learning means only unsupervised systems (not unique to deep learning), or systems which automatically discover all features (not unique to deep learning), or simply that it’s synonymous with deep neural nets, or more specifically convolutional neural nets or recurrent neural nets (including LSTM).
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For the latest techniques in and revealing case studies about machine learning and predictive analytics, attend Predictive Analytics World for Business Las Vegas, June 3-7, 2018.
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