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Home » Wrangling Data for Your Machine Learning Models
Hyperautomation Minute

Wrangling Data for Your Machine Learning Models

Enterprise AI Minute Ep 40
Paul SwiderBy Paul SwiderApril 28, 2022Updated:May 28, 20221 Min Read
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This episode is brought to you by the Cloud Wars Expo. This in-person event will be held June 28th to 30th at the Moscone Center in San Francisco, California.

Highlights

00:20 — Data wrangling is the process of normalizing—or, cleaning up—your data for machine learning.

00:45 — Paul shares an example of a project that’s he’s been working on involving a 10-year dataset focused on finance data and productivity data in healthcare. Looking at the dataset, they identify fields that are missing key pieces of information.

01:15 — Another example is de-identifying data. This could be for security concerns as well as help with bias in the data.

01:50 — In some cases, you’re usuing data science and machine learning techniques to fill in the fields with missing data.

02:17 — Once the data wrangling process is done, you will, ideally, end up with a complete dataset that you can use to build and train your machine learning models.

02:28 — The first steps of data wrangling may require strong data talent and resources before you send off the datasets to your data scientists.


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Paul Swider
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Paul Swider is an Acceleration Economy Analyst focused on healthcare technology and the Chief Technology & AI Officer for RealActivity, a Boston-based SaaS startup, focused on streamlining healthcare operations and improving the patient-provider experience. Paul is also the Founder and community engagement lead for the Boston Healthcare Technology User Group. Paul is passionate about speaking at international conferences in his spare time, and he occasionally gets to chase the tides and winds as an avid sailor with his family and friends in Puerto Rico. -`ღ´-

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