<br><div class="gmail_quote">On Thu, Jan 6, 2011 at 9:43 AM, Venkatraman S <span dir="ltr"><<a href="mailto:venkat83@gmail.com">venkat83@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<br>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.<br>
</blockquote><div><br>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.<br>
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?<br>To give you one more idea,<a href="http://stats.stackexchange.com/questions/5960/how-to-identify-a-bimodal-distribution"> i asked this question in stackoverflow</a>.Working(handson) on data while understanding/learning them is a great way to learn :)<br>
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