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Statistical Modeling versus Machine Learning

Recently I got to read up on machine learning, more of introductory and elementary stuff.  The more I read, the more similarity (and of course differences as well) I found as compared to statistics.  This post I found (Statistics vs. Machine Learning, fight!) shed more light on the two topics.

Pattern Recognition, Neural Networks, Machine Learning, Graphical Models, Data Visualization, Big Data, Distributed Data Analysis, Parallel Computing… all seem to be related “technology”.  The core interest, however, is data analysis:

The field should be defined in terms of a set of problems — rather than a set of tools — that pertain to data.

So now, how do I get there?



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