Data Scientists Sometimes Fool Themselves

@machinelearnbot

The easiest person in the world to fool is yourself. Thus, I strongly suggest that we acknowledge real or subconscious biases in ourselves, the data, the analysis and group think. It is prudent for data science teams to have both internal and external checks and balances to expose potential biases and better understand objective reality. Raw data sets - both large and small - are not objective - they are selected, collected, filtered, structured and analyzed by human design. What was measured, in what manner, with what devices and to what purpose?


Taking the pulse of machine learning adoption ZDNet

#artificialintelligence

A few months back, we gave our take on a survey from the O'Reilly folks regarding interest in deep learning. The survey reported that interest was more than latent, but there's little question that the bulk of the action today is in the (relatively) better understood confines of machine learning (ML). So on this go round, O'Reilly jumped into the shallower side of the pond to survey the people who subscribe to its publications and go to its big data-related Strata and AI conferences regarding ML. Before diving in, let's put some perspective on this cohort: it's likely a group that on average is ahead of the curve by virtue of its attendance at these big data events or consumption of O'Reilly learning services that are skewing increasingly toward the AI domain. Nonetheless, it provides a useful counterpoint to their earlier work exploring interest in deep learning.


Taking the pulse of machine learning adoption

ZDNet

A few months back, we gave our take on a survey from the O'Reilly folks regarding interest in deep learning. The survey reported that interest was more than latent, but there's little question that the bulk of the action today is in the (relatively) better understood confines of machine learning (ML). So on this go round, O'Reilly jumped into the shallower side of the pond to survey the people who subscribe to its publications and go to its big data-related Strata and AI conferences regarding ML. Before diving in, let's put some perspective on this cohort: it's likely a group that on average is ahead of the curve by virtue of its attendance at these big data events or consumption of O'Reilly learning services that are skewing increasingly toward the AI domain. Nonetheless, it provides a useful counterpoint to their earlier work exploring interest in deep learning.


Taking the pulse of machine learning adoption ZDNet

#artificialintelligence

A few months back, we gave our take on a survey from the O'Reilly folks regarding interest in deep learning. The survey reported that interest was more than latent, but there's little question that the bulk of the action today is in the (relatively) better understood confines of machine learning (ML). So on this go round, O'Reilly jumped into the shallower side of the pond to survey the people who subscribe to its publications and go to its big data-related Strata and AI conferences regarding ML. Before diving in, let's put some perspective on this cohort: it's likely a group that on average is ahead of the curve by virtue of its attendance at these big data events or consumption of O'Reilly learning services that are skewing increasingly toward the AI domain. Nonetheless, it provides a useful counterpoint to their earlier work exploring interest in deep learning.


Sampling: A Primer

@machinelearnbot

Kevin Gray: Sampling theory and methods are part of any introductory statistics or marketing research course. Just as a review, can you give us a layperson's definition of sampling and tell us what it's used for? Stas Kolenikov: Sampling is used when you cannot reach every member of your target population. It's used in virtually all marketing research, as well as most social, behavioral and biomedical research. Research projects have limited budgets but, by sampling, you can obtain the information you need with maybe 200 or 1,000 or 20,000 people – just a fraction of the target population.