Prepping Geo-Location Data for Machine Learning
In my last two posts, I described the techniques we used to create and enhance an ML data set for supervised learning. In my next post, we finally get to the really fun stuff – building models. But...
In my last two posts, I described the techniques we used to create and enhance an ML data set for supervised learning. In my next post, we finally get to the really fun stuff – building models. But...
The Measurement Minute #49 with Gary Angel : Ugly Isn't Always a Dirty Word A recent article in Slate magazine on the Trump campaigns brutally ugly emails not only brought a lump of nost...
The Measurement Minute #47 with Gary Angel : Don't be a Friggin Moron - Choosing a Cloud Provider for Retail Some IT decisions aren't really about IT. And if you're a retailer, choosing ...
There’s a rich history around the idea of data augmentation for Machine Learning. If you have a library of images you want to train a model on, you can use simple image manipulation techniques to c...
The Measurement Minute #46 with Gary Angel : Too Sexy to be Useful - Data Science in the Enterprise Have enterprise's sacrificed too much in trying to make data science jobs as sexy as p...
The Measurement Minute #44 with Gary Angel : Operationalizing ML Like most things, we tend to focus on the fun and interesting part of machine learning - creating models. But in many res...
We use a variety of sensors to track shopper movement in stores. And to do that more accurately, we re-engineered our processes to take advantage of machine learning. You can get the full skinny on t...
The Measurement Minute #43 with Gary Angel : The Funnel is Dead Customer Journey's are mostly thought of as a linear funnel. It's a common-sense view that helps make a complicated proces...
To understand why ML became the centerpiece of our location analytics strategy you have to understand why we had a problem in the first place. In this post, I’m going to describe how traditional lo...