My coworker asked me for a "good intro to ML."
I would like to point him to a "ready to go" (Collab, Kaggle, Huggingface) notebook with good annotations/ explanations between the code blocks.
Can you recommend one? Something along the lines of this but with a direct link to a notebook, ready to run and with more expository text:
https://github.com/ssomani7/California_Housing_Prices/blob/master/Housing.ipynb
Go to kaggle.com. Sign-up / Sign-in as necessary. Click the "code" tab in the left hand navigation bar. On the ensuing page, select the "beginner" filter, and then change the sort to "by most votes".
https://www.kaggle.com/code?sortBy=voteCount&tagIds=13102
The top few results there will probably be good for your friend.
This Titanic one looks pretty good, but note that it uses R, which might be off-putting to some people.
https://www.kaggle.com/code/mrisdal/exploring-survival-on-th...
But browse around, there are several that look pretty comprehensive and well documented at a first glance.
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Also, if your friend wants a book to go along with their learning, look at "Hands On Machine Learning" by Geron. It's a great book anyway, AND the Github repo includes a whole series of Jupyter notebooks to go along with the book, including a handy "Open in Colab" link that will launch the notebooks directly in Colab.
See:
https://github.com/ageron/handson-ml3
https://www.oreilly.com/library/view/hands-on-machine-learni...