Once data has been stored in Hadoop, the next logical step is to extract useful information/patterns on which some action can be taken and also to make the machines learn from the vast amount of data. Storing and retrieving raw data is of not much use. Frameworks like Apache Mahout, Weka, R, ECL-ML implement a lot of Machine Learning algorithms. Though Machine Learning is not new, it had been picking up lately because vast amount of data can be stored easily and the processing power is also getting cheaper. Here are some nice articles on the same.
Machine Learning makes it possible for Google Picasa to identify faces in pictures, for GMail to identify spam in mails, for friends recommendations in LinkedIn, for books recommendations in Amazon, for search engines to show relevant information and a lot of other useful things.
I have included a new page for `Machine Learning` where I would be updating with useful and interesting articles/books/blogs/tutorials and other information which would be useful for those who are getting started with Machine Learning. I would also be more frequently blogging about `Machine Learning` here.
I am starting with the Mahout in Action book and this Coursera tutorial.
Machine Learning makes it possible for Google Picasa to identify faces in pictures, for GMail to identify spam in mails, for friends recommendations in LinkedIn, for books recommendations in Amazon, for search engines to show relevant information and a lot of other useful things.
I have included a new page for `Machine Learning` where I would be updating with useful and interesting articles/books/blogs/tutorials and other information which would be useful for those who are getting started with Machine Learning. I would also be more frequently blogging about `Machine Learning` here.
I am starting with the Mahout in Action book and this Coursera tutorial.
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