good refresher, 😇

This is a very well constructed lecture and reproducible (all the data and source code is given on github). A nice scaffold to build your own lectures on and expand. My only complaint is that the transitions are often not clear and bit rugged (e.g. spend a lot time on LASSO but then use Logistic Regression).

If you are worried about which columns are picked in step_normalize() and you all want columns with values greater than 1 then I believe this code works: step_normalize(where(~is.numeric(.x) && any(.x > 1))). Now what the author uses in the video is more straight forward and thus simpler but if you have a lot of columns the first approach might be safer.

If you are starting out with tidymodels then yo might be confused since a little of details are left out. Naturally you cannot cover such a big subject in a short lecture. Those needing more information might want to look at "Tidy Modeling with r" by Kuhn and Silge(2022). A free ebook version is available online.