by | Released May 2011 | ISBN: 0636920017516

Once you've accumulated a pile of data through your web application, what do you do with it? In this insightful video course, bit.ly lead scientist Hilary Mason shows you how to solve data analysis problems using basic machine learning techniques and frameworks. You'll follow several examples through the entire process—from obtaining, cleaning, and exploring data to building a model and interpreting the results.Examine several real-world analysis solutions, including supervised learning and classification, unsupervised learning and clustering, and building common machine learning applications such as recommendation systems. If you're a developer interested in the math and processes necessary to apply machine learning techniques to web data, this video course is for you.Intended Audience:This class is intended for developers who are interested in an introduction to the math and processes to apply machine learning techniques to web data. Show and hide more Publisher resources Download Example Code

1. Introduction 00:20:25
2. Classifying Web Documents - The Theory 00:29:23
3. Classifying Web Documents - The Code 00:47:57
4. Clustering, Recommendations, and Probability 00:54:07
5. Conclusion 00:11:30
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