Mini rant:
Not to call out anyone in particular, but I've noticed an extremely misleading pattern in these "getting started with ML" type guides. 😬
Learning how to do data visualization isn't a "fun extra" if you're getting into ML: it's absolutely essential.
Anyway, if you're looking for a good very intro guide, I like:
The R for Data Science chapter for R: https://r4ds.had.co.nz/data-visualisation.html
The @swcarpentry@twitter.com intro to Python: https://swcarpentry.github.io/python-novice-inflammation/
(Notice that plotting is basically the first thing taught in these courses! Not a coincidence!)
Like I'm not too bothered about what sort of tool you're using but at a *minimum* you should be able to make:
1. Frequency plots (finding outliers/anomalies, corr. matrices, etc.)
2. Time series visualizations (test/train error)
3. Plot errors (AUC is a *standard measure*)