Tag: machine learning

Now, Kirsten Westeinde & Rich Jolly

Processed with VSCO with c1 presetUsing Your Data to Give Your Customers Superpowers

Kirsten Westeinde (@kmkwesteinde) is a technology enthusiast and a lifelong learner. Currently a senior software engineer at Shopify, where she solves challenging web development problems on the daily. She’s toiled and built apps and services with Ruby on Rails for greater than 5 years and is constantly adding new tools to her tool belt. In the important realm of building us humans, she is passionate about building diverse teams, mentoring & teaching, and gaining new perspectives through travel & cross cultural communication.
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Say Hello to Jon and Amy @ML4ALL!

Welcome the next two speakers I’m introducing: Jon Oropeza and Amy Cheng!

jon-oropezaML Spends A Year In Burgundy

Jon (@joropeza) is coming to Portland from Portland to speak to us about Cote d’Or in Burgundy! He’s a hacker, grape lover, and Portlander that loves a good smooth wine made out of those grapes he loves!

Jon being a both a weather nerd and a wine nerd, he was curious if machine learning could be applied to vintages in an area where a) quality of wine varies greatly by year b) most of that variance has to do with weather patterns and different aspects of temperature and precipitation c) there are known, reasonably-objective classifications or ‘scores’ of each vintage, such that we could say that such-and-such year with such-and-such weather produced wines of good/bad/mediocre quality.

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@ML4ALL Meet Clair J. Sullivan & Ricky Hennessy

ricky-hennessyUsing Machine Learning to Increase Health Insurance Coverage

Ricky (@rickyhennessy) works as a Sr. Data Designer at Fjord, Design and Innovation from Accenture. Previously, he was at frog design, where he worked as a Sr. Data Scientist. Working at the intersection of data science and design, he’s been able to apply a more human centered approach to data science. He has also earned a PhD in biomedical engineering at UT Austin.

Working with a state run healthcare exchange, Ricky & team utilized their existing data to develop a machine learning model that could predict whether or not an individual was going to sign up for insurance through the exchange in the next open enrollment enrollment period. This model can then be used to inform outreach campaigns targeted at individuals at risk of dropping out of the exchange.

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ML4ALL Speakers – Meet Paige & Suz

Hello everybody and especially the upcoming ML4ALL attendees! For those that haven’t registered, here’s the link for that, and for those that have welcome aboard! Here’s two of the excellent speakers that will be presenting at ML4ALL Paige (@DynamicWebPaige) and Suz (@Noopkat)!

Paige & Suz both work on the amazing, now somewhat famous Microsoft Developer Advocacy team others like Peter Bright (@drpizza) wrote about in “Microsoft’s renewed embrace of developers developers developers” and James Governor (@monkchips) wrote in “On Hiring Jesse Frazelle: Microsoft’s developer advocacy hot streak continues” and I even wrote a blurb about many months ago. They’ve added even more awesome to a great team and will be coming to ML4ALL to do more of that.

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ML4ALL (Machine Learning for All)

Alright, I’m sure everybody has a vast and expansive understanding of machine learning and all the algorithms involved and…

oh wait…

…not really? Yeah, me neither. Alright, I’ve got a conference for you. The ML4ALL Conference is lined up for May 27–29th where a reasonably sized group of machine learning practitioners and completely new to the field people are going to gather to speak, learn, discuss, and aspire to more and better machine learning. We’d love for you to join us, come speak on a machine learning topic dear to your heart, and enjoy the city, sites, cuisine, and relaxed nature of Portland while you’re at it. Continue reading “ML4ALL (Machine Learning for All)” →