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Machine Learning

Advice I’d Send Myself Before Starting My Machine Learning Internship at Factual

I spent this summer as a Data Specialist Intern at Factual, and was tasked with improving our Global Places categorization. Factual employs a wide variety of strategies at every stage of its data pipeline, and categorization is just one part of that. To clarify, every Factual Place belongs to one category from our 400+ node taxonomy....

The Wisdom of Crowds: Using Ensembles for Machine Learning

Whether it’s computing Point of Interest similarities for our Resolve service, or predicting business categories from business names, we at Factual use Machine Learning to solve a wide variety of problems in our pursuit of quality data and products. One of the most powerful Machine Learning techniques we turn to is ensembling. Ensemble methods build surprisingly...

A Brief Tour of Factual’s Machine Learning Pipeline

In my previous post, 5 Principles for Applying Machine Learning Techniques, I reviewed the principles for putting machine learning to work. In this blog, I will present a brief guided tour of the pipeline that translates that theory into action. At the beginning of our pipeline, we have an ocean of data coming at us in...

5 Principles for Applying Machine Learning Techniques

Here at Factual we apply machine learning techniques to help us build high quality data sets out of the gnarly mass of data that we gather from everywhere we can find it.  To date we have built a collection of high quality datasets in the areas of places (local businesses and other points of interest) and...