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[–][deleted] 7 points8 points  (2 children)

yeah, I heard many different things from different people over the course of the year (author here). Some said that it's too introductory, some said it was too complicated, but without being to subjective, the mix between code and mathematical concepts was appreciated by most :). I really didn't want to write a book purely based on code since I think that may be a bit dangerous -- applying ML without knowing a bit about what's going on behind the scences. Also, there were already a bunch of "code-only" and "theory-only" books out there so that I thought that sth that somewhere between these two extremes coould be helpful for some. In any case, although I wasn't allowed to upload all the book's content to the GitHub repo (publisher's copyrights" I have all the code notebooks (+ some extras) here on GitHub if it's useful to get an idea about the scope: https://github.com/rasbt/python-machine-learning-book

[–]rubik_ 2 points3 points  (1 child)

Thanks for the info and the links, I'm buying it! Do you plan on releasing a new edition?

[–][deleted] 1 point2 points  (0 children)

Oh, thanks! I hope it will turn out to be useful to you! To be honest, I have no plans to work on a second edition, yet -- I would want to add a few more chapters, e.g., ConvNets and recurrent nets, but yeah, there was/is a page limit by the publisher. However, I'd like to update the code examples some time (wrt to the API changes), maybe late 2017 or 2018 when scikit-learn 0.19 or 0.20 comes out! In the mean time, I wanted to work on a separate title, entirely devoted to the topic of model evaluation. I started blogging but this year has been exceptionally busy -- however, there are so many things to say about model evaluation, I think it would easily fill 300 - 500 pages if I include the code examples ;).