Machine Learning Field Guide
kamwithk.com
kamwithk.com
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.
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It will typically include a description of the objects covered, together with paintings or photographs and an index. More serious and scientific field identification books, including those intended for students, will probably include identification keys to assist with identification, but the publicly accessible field guide is more often a browsable picture guide organized by family, colour, shape, location or other descriptors.
This barely qualifies as a field guide. A survivalist would have thrown a wildlife field guide of this quality in the trash, or on the store shelf.