# Posts Tagged ‘ machinelearning ’

## Code for Machine Learning for Hackers

February 16, 2012
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With the release of the eBook version of Machine Learning for Hackers this week, many people have been asking for the code. With good reason—as it turns out—because O’Reilly still (at the time of this writing) has not updated the book page to include a link to the code. For those interested, my co-author John Myles

## Machine Learning Ex3 – Multivariate Linear Regression

March 29, 2011
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Part 1. Finding alpha. The first question to resolve in Exercise 3 is to pick a good learning rate alpha. This require making an initial selection, running gradient descent and observing the cost function. Read More: 221 Words Totally

## Machine Learning Ex2 – Linear Regression

March 22, 2011
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Thanks to this post, I found OpenClassroom. In addition, thanks to Andrew Ng and his lectures, I took my first course in machine learning. These videos are quite easy to follow. Exercise 2 requires implementing gradient descent algorithm to model data with linear regression. Read More: 243 Words Totally

## Machine Learning Ex5.2 – Regularized Logistic Regression

March 20, 2011
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Exercise 5.2 Improves the Logistic Regression implementation done in Exercise 4 by adding a regularization parameter that reduces the problem of over-fitting. We will be using Newton's Method. Data Here's the data we want to fit. # linear regression # load the data mydata = read.csv("http://spreadsheets.google.com/pub?key=0AnypY27pPCJydHZPN2pFbkZGd1RKeU81OFY3ZHJldWc&output=csv", header = TRUE) # plot the data plot(mydata$u, mydata$v,, xlab="u", ylab="v") points(mydata\$u,...

## Machine Learning Ex5.1 – Regularized Linear Regression

March 18, 2011
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Exercise 5.1 Improves the Linear Regression implementation done in Exercise 3 by adding a regularization parameter that reduces the problem of over-fitting. Over-fitting occurs especially when fitting a high-order polynomial, that we will try to do here. Data Here's the points we will make a model from: # linear regression mydata = read.csv("http://spreadsheets.google.com/pub?hl=en_GB&hl=en_GB&key=0AnypY27pPCJydGhtbUlZekVUQTc0dm5QaXp1YWpSY3c&output=csv", header = TRUE) # view data plot(mydata)

## Machine Learning Ex4 – Logistic Regression and Newton’s Method

March 16, 2011
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Exercise 4 is all about using Newton's Method to implement logistic regression on a classification problem. For all this to make sense i suggest having a look at Andrew Ng machine learning lectures on openclassroom. We start with a dataset representing 40 students who were admitted to college and 40 students who were not admitted, and their corresponding...

## Language used by Academics with the Protection of Anonymity

March 14, 2011
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Those in the political science discipline probably remember their first encounter with poliscijobrumors.com. For those outside, you have probably never heard of this particular message board, and you would have no reason to. As the URL suggests, the board specializes in rumor, gossip, back-bitting, mudslinging, and the occasional lucid thread on the political science

## Machine Learning Ex3 – multivariate linear regression

March 8, 2011
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Exercise 3 is about multivariate linear regression. First part is about finding a good learning rate (alpha) and 2nd part is about implementing linear regression using normal equations instead of the gradient descent algorithm. Data As usual hosted in google docs: mydata = read.csv("http://spreadsheets.google.com/pub?key=0AnypY27pPCJydExfUzdtVXZuUWphM19vdVBidnFFSWc&output=csv", header = TRUE) # show last 5 rows tail(mydata, 5) area bedrooms price 43 2567 ...

## Visualizing the Language Used by Academics when Protected by Anonymity

March 7, 2011
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Those in the political science discipline probably remember their first encounter with poliscijobrumors.com. For those outside, you have probably never heard of this particular message board, and you would have no reason to. As the URL suggests, the board specializes in rumor, gossip, back-bitting, mudslinging, and the occasional lucid thread on the political science