[Baypiggies] Suggested Reading: Machine learning and Python

[Venkatraman S]

On Thu, Jan 6, 2011 at 9:43 AM, Venkatraman S <venkat83 at gmail.com> wrote:

>
> I have gone through Collective Intelligence and Python text Processing with
> NLTK, and was wondering whether there is any suggested reading to get into
> deeper depths in statistical machine learning - something which gives a
> 'basic' introduction to mixture models , EM etc.
>

Let me expatiate a little more before you point me to the Measuring Measure
link or Andy Ng classes : I have seen them and have attended the first 3
lectures of Andy. I also went through courses taught at MIT(via OCW) and
Jordan's classes(UCB) - but most of these stuff are heavy theoretical - not
that I am against theory, but i want some hands-on to understand how theory
is implemented.
For eg. PythonTextProcessingUsingNLTK does a great job in understanding
various aspects of text processing by playing with text parallely. Is there
something similar to understand kernel methods or mixture models?
To give you one more idea, i asked this question in
stackoverflow<http://stats.stackexchange.com/questions/5960/how-to-identify-a-bimodal-distribution>.Working(handson)
on data while understanding/learning them is a great way to learn :)
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