[giu]

I was thinking the same; there are so many articles explaining the basics.

For me it would be more helpful to start off with a real-life scenario where the mentioned method can be applied and even might excel compared to other methods; bonus points if you also explain what properties of the method make it so very well-suited for the specific real-life scenario.

There are so many methods in data science / machine learning and from what I remember from my university days one of the difficult tasks was to know when to use which method, depending on the properties of your data and on what you want to achieve; additionally, sometimes you also need to optimize/improve the method's hyperparameters and that's almost a whole separate discipline by itself.

Nonetheless, the posted article contains a lot of valuable information for a beginner, so it's definitely a good start.