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I've taken an ML course previously, but now that I am working with ML related projects at my job, I am struggling quite a bit to actually apply it. I'm sure the stuff I'm doing has been researched/dealt with before, but I can't find specific topics.

All the machine learning examples I find online are very simple (e.g. how to use a KMeans model in Python and look at the predictions). I am looking for good resources on how to actually apply these, and maybe code examples of large scale machine learning implementations and model trainings. I want to learn about how to effectively process and create new data that can make the ML algorithms much more effective.

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I do not have knowledge in ML. After a little web searching, I found a reddit thread that lists the following books - all of which are legally downloadable for free. You can research the titles of your interest for details. Also comment if you find any of the books helpful (and why).

Machine Learning

Probability / Stats

Linear Algebra / Optimization

Genetic Algorithm

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    $\begingroup$ Thank you for the links. I am in the process of checking some of them out, but what I'm more interested in is something which gives practical examples of using the algorithms and training a model. I already have a good basis for how they work mathematically so I don't need to to understand that as much anymore. Given that I have functional models for each of the algorithms (which are widely available), I want to learn how to use these functions to effectively develop a model. $\endgroup$ – Stoneman Jun 8 '16 at 17:52
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One of the books that I would recommend is Introduction to Statistical Learning and it is free to download. This book is easy to follow with exercises in R. Another good one is Applied Predictive Modeling

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I think it would be better to follow proceedings of some machine learning-related conferences. Such conferences usually have application tracks, where you can find practical applications of machine learning algorithms.

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