Ben for short. Experienced in end-to-end data science & engineering using Python, but I consider myself a generalist. Deeply excited about autonomy and cryptocurency (disjointly). Standing up out of chairs whenever possible. Keeping beginner's mind as best as I can.
Mar 20, 2018

What Is Machine Learning 2.0?

Today when enterprises want to use machine learning to predict future outcomes — what will a customer buy next? will sales increase next month? is a piece of manufacturing equipment likely to fail soon? — it takes a village to develop a machine learning based solution. As the demand from businesses to leverage machine learning continues growing at an exponential rate, the current time-intensive process that heavily relies on highly-skilled ML experts won’t suffice.

(External post: featurelabs.com)

Mar 6, 2018

Getting Value from Machine Learning Isn’t About Fancier Algorithms — It’s About Making It Easier to Use

Machine learning can drive tangible business value for a wide range of industries — but only if it is actually put to use. Despite the many machine learning discoveries being made by academics, new research papers showing what is possible, and an increasing amount of data available, companies are struggling to deploy machine learning to solve real business problems. In short, the gap for most companies isn’t that machine learning doesn’t work, but that they struggle to actually use it.

(External post: hbr.org)

Jan 2, 2018

Feature Engineering vs Feature Selection

All machine learning workflows depend on feature engineering and feature selection. However, they are often erroneously equated by the data science and machine learning communities. Although they share some overlap, these two ideas have different objectives. Knowing these distinct goals can tremendously improve your data science workflow and pipelines.

(External post: featurelabs.com)

Oct 25, 2017

Applying Data Science Automation to Better Predict Credit Card Fraud

If you use a credit card, you probably know the feeling of having your card declined due to a suspected fraudulent transaction. An industry report from 2015 found that one out of every six legitimate cardholders experienced at least one declined transaction because of inaccurate fraud detection in the past year. That makes fraud detection an expensive problem for issuers: Those declined transactions lead to nearly $118 billion dollars in losses on an annual basis.

(External post: featurelabs.com)

Oct 10, 2016

Lil' Neuron Takes His First Few Chaotic, Rectified Breaths

Three verses in and I realized I was in way over my head. The rap game is an endurance sport, and the ones at the top started running years ago.