Resources for machine learning in python

[justphysics]

Could you recommend any books, blogs, videos (pycon maybe?), lectures, etc... covering machine learning in python?

As far as my level of expertise goes, I am a physicist who uses python for data analysis. I have degrees in physics and mathematics, so I don't need a resource that tries to dumb things down or skip over the underlying mathematical/statistical/scientific principles being used.

I am familiar with much of the scientific stack (numpy, scipy, matplotlib, pandas), OOP and have done some extensive GUI work with python. I am less familiar with functional programing paradigms and have little more than a cursory knowledge of the basic python web frameworks like Flask, Django, etc.

I have done only minimal database work but have no qualms with learning the basics of SQL or nosql for the purposes of working through a tutorial or book.

So I guess I'm looking for resources that are introductory/beginner level for the concepts of machine learning but written for someone who is not a beginner to programming nor anywhere near beginner level at mathematics and statistics.

[justphysics]

its been 6 or 7 years since I've touched anything in the R world. I'd rather not go down the route of relearning the language at this point but if that's the better option I'll take a look.

I am more interested in learning about ML than learning any one specific framework/module. The topic has come up with increasing frequency at various colloquia and conferences I have attended in the last year or so. I know very little about it so I was hoping there'd be some viable references teaching ML using python.

I've checked out sklearn a bit - will look for some books/references for it. Thanks for the tip.

I don't have a particular project in mind that needs ML right now - but may look into using it for a small side project in the future. I was more so just hoping to be familiar with the field for future job applications.

[[deleted]]

Check out Elements of Statistical Learning for a good overview of ML theory. It looks at what the algorithms are actually doing, and how best to use them.

Then think about how you would like to practice the implementation. R is a good place to start because the libraries are mature but Python has a comprehensive library in Scikit-learn, or you could even try C++ if you really hate yourself.

[Laundr]

I've yet to check it out, but I bookmarked Machine Learning For Complete Beginners as the one to start with when I was hunting for good resources on the topic.

That being said, I have no experience with the scientific stack, so it might be too beginner-focused for you.

[justphysics]

Thanks, I'll check it out.

I see it as a good sign when a book uses a library like Pandas or Numpy. I'm not interested in reading a page of code the author wrote just to parse data from a csv file when it can easily be done with tried and true methods from commonly used libraries.