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Helpful resources:
✨ Github: github.com/sim...
✨ Follow-up Q&A Session: kzbin.info...
✨ If you want to book a call with our team to chat more about Posit products: pos.it/chat-with-us
✨ Don't want to meet, but curious who else on your team is using Posit? pos.it/connect-us
✨ Blog post on tidymodels + Posit Connect: posit.co/blog/...
✨ Tidy Modeling with R book: www.tmwr.org/
Timestamps:
1:44 - Three steps for developing a machine learning model
3:35 - What is a machine learning model?
7:02 - Overview of machine learning with Posit Team
7:36: Step 1: Understand and clean data
11:05 - Step 2: Train and evaluate models (why you might be interested using tidymodels)
23:02 - Step 3: Deploying a machine learning model from Posit Workbench to Posit Connect
30:14 - Summary
31:21 - Helpful resources
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Machine learning models are all around us, from Netflix movie recommendations to Zillow property value estimates to email spam filters.
As these models play an increasingly large role in our personal and professional lives, understanding and embracing them has never been more important; machine learning helps us make better, data-driven decisions.
The tidymodels framework is a powerful set of tools for building-and getting value out of-machine learning models with R.
Data scientists use tidymodels to:
1. Gain access to a wide variety of machine learning methods
2. Guard against common mistakes
3. Easily deploy models through tidymodels’ integration with vetiver
Join Simon Couch from the tidyverse team on Wednesday, October 25th at 11am ET as he walks through an end-to-end machine learning workflow with Posit Team.
No registration is required to attend - simply add it to your calendar using this link: pos.it/team-demo