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I've started reading Hands on Machine Learning and wanted to share one of my #learnML reading strategies!
As I read I interrogate the text, and write my questions in a notebook to answer either as I come across them in the text, or to research when I have a couple of minutes.
As I read I interrogate the text, and write my questions in a notebook to answer either as I come across them in the text, or to research when I have a couple of minutes.
I develop questions by looking for small words or phrases that suggest there are alternatives---things like "may be" and "one way".
these words and phrases point to the idea that what's being said isn't absolute---so there's information the author left out that I can research.
these words and phrases point to the idea that what's being said isn't absolute---so there's information the author left out that I can research.
for example, the two questions I wrote down for this section (on unsupervised learning) are:
--what are alternatives to feature extraction?
-- is dimensionality reduction only done in unsupervised learning?
--what are alternatives to feature extraction?
-- is dimensionality reduction only done in unsupervised learning?
