dafrdman

Thanks so much! I would say two major differences: 1, as you mention, it codes each method up from scratch in Python readers can really see each step the method uses. 2, it is focused on the derivations of these methods, rather than their intuition, applications, etc.

It's definitely not deep learning focused. I wanted to start by introducing the models machine learning practitioners should all know. But collaborative filtering and stuff along those lines would be a good addition! Thanks for the feedback.

My hesitance with "Application" is that sounds like I'm going to use some interesting dataset or do some cool project (and this is essentially using iris to build basic models). How about "code" becomes "implementation"?

That's sensible. Maybe change construction to code and code to application? Or keep construction but rename code? I'll have to brainstorm. I definitely don't want people missing the construction section so this is great feedback. Thanks!

I definitely agree. I should add more comments explaining what things like .T does--it's not that it's hard to grasp, but it might turn away newbies. Thanks for the suggestion!

Pandas is only used in the "code" sections, which use packages like scikit-learn anyway

Agreed. That's #1 on my list right now.

I agree though I saw that as outside the scope of this book. I tried to be clear in the introduction that the book is a "user manual" of sorts that simply shows how to construct models, rather than how to decide between them, what the benefits of each are, etc. That information is certainly important but I felt it had been covered more than adequately by books like Elements of Statistical Learning

Perhaps I should have been clearer, but the "code" section within each chapter is not "from scratch". The "construction" section is "from scratch" in that it only uses numpy (not scikit learn). The scikit-learn part is just so new users can see how these could be fit in practice.

Good question. I definitely prefer downloadable books myself. I made it in JupyterBook because that was easiest with the executable ipynb files. I'll look into whether I can make it downloadable and update you if so.

Good ideas. I think I'll try to add an appendix, minimize the number of numpy functions used, and explain any of the weird ones that are real time savers. Thanks for all your thought.

I hadn't even considered licensing it. Want to email me and we can talk? My email is [email protected]. That said, you're welcome to use it (though my lawyer father suggests I say that this "verbal contract" is revocable and non-exclusive).

Yeah the book was built with JupyterBook. It's an awesome tool but I lose track of what it does to the .md files when creating the website.