Check out the next post in the series – http://conductrics.com/data-science-resources-2 . I have a few links on clustering and RL, plus a shout out to Stephen Boyd and his convex optimization course. His class is kinda awesome actually, esp if you already have some ML behind you, since he approaches it from a different angle. ]]>

thanks for your collection. I have a question: I’m a physics student and due to my research topics (stochastic processes) I want to learn machine learning. I know how to programm, but I have never done anything ML-related before. There are also no classes/lectures for ML at my university, so I feel a little bit lost. You have posted great material, but most of it is video material. Could you give me a guide-line where I should start for the very basics? Is there THE book you recommend on machine learning?

Looking forward to get a reply. Best greetings,

Ben

Thx for the links. I totally agree with you that linear algebra is essential to all things machine learning.

I working on a book that explains all of LA in a simple and intuitive way — in just 60 pages. Check it out, and feel free to send anyone who is lacking in LA skills my way:

minireference.com/linear_algebra/introduction (free for now while fixing typos, available in print in Jan)

I would love to hear what you think.

Peace out,

Ivan

]]>I’m not so sure about other areas but for Statistical Machine Learning, I completely agree that Philip Koehn is definitely the guy to go for. I’m using his Moses package as well.

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