Why R? Keynote Kick Off – Julia Silge – Today 5pm UTC

[This article was first published on http://r-addict.com, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Data visualization for machine learning practitioners is the title of Why R? Keynote Kick Off by Julia Silge that starts today at 5pm UTC!. You can find all scheduled talks on our youtube.com/WhyRFoundation channel.

The talk will start the Text Mining Hackathon 2020.whyr.pl/hackaton/. At the time of the talk we will announce challenges for the hackathon for which you have 24 hours to propose solutions to. You can still register to the hackathon

Below you can find biogram of Julia and the abstract of the talk.

Here is the direct url to the stream.

Bio: Julia Silge is a data scientist and software engineer at RStudio PBC where she works on open source modeling tools. She is an author, an international keynote speaker, and a real-world practitioner focusing on data analysis and machine learning practice. Julia loves text analysis, making beautiful charts, and communicating about technical topics with diverse audiences.

Description: Visual representations of data inform how machine learning practitioners think, understand, and decide. Before charts are ever used for outward communication about a ML system, they are used by the system designers and operators themselves as a tool to make better modeling choices. Practitioners use visualization, from very familiar statistical graphics to creative and less standard plots, at the points of most important human decisions when other ways to validate those decisions can be difficult. Visualization approaches are used to understand both the data that serves as input for machine learning and the models that practitioners create. In this talk, learn about the process of building a ML model in the real world, how and when practitioners use visualization to make more effective choices, and considerations for ML visualization tooling.

To leave a comment for the author, please follow the link and comment on their blog: http://r-addict.com.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)