Half of the article is massive, meaningless images. As in, "a girl in the woods wearing sunglasses staring up at a tree" so big it takes up an entire length of my screen - massive.

It took me nearly 30 seconds to scroll down the length of it. I counted the actual lines of code in the article, and it amounts to 27.

The only substance of the article shows loading a CSV file into Python and using a few SKLearn functions on them, with a handful of paragraphs that amount to essentially docstrings of the methods themselves without any actual explanation.

The content on Linear/Logistic Regression, Support Vector Machines, Neural Networks, Decision Trees, K-Means, and Random Forests all fit in the same screenlength.

I'm not sure that level of brevity of information is genuinely helpful to someone.

"Decision trees can be used for regression problems too. Although simple, to avoid overfitting, several hyperparameters must be chosen. These all, in general, relate to how deep the tree is and how many decisions are to be made."

There's no prior given for the context of what a "hyperparameter" is, how it's different than a parameter, or what the problem of "overfitting" is.