First Kaggle Competition

2014-10-14 19:07

This Tutorial is from:

Kaggle Python Data Science

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Recently, i have entered Kaggle competition for data scince. i have ranked 342 out of almost 800 other competitors. Pretty impressive eh? Here's how i got to it.

Boosting, Post-decessor

2014-10-14 05:13

Boosting, Post-decessor

This is incorrect, as weakest can do better than chance, then at least all weak learner will perform the boosting just fine.

Support Vector Machines

2014-10-14 02:48

Support Vector Machines

This is the SVM

Boosting

2014-10-13 12:39

Boosting

So instead of taking uniformly randomly subsets of data, take "hardest examples" from it

Boosting, Pre-decesor

2014-10-13 10:49

Boosting, Pre-decesor

Now we're implementing something called spam classfier.

kNN

2014-10-10 09:59

kNN

So this is the kNN algorithm that based on the housing prices example

Instance Based Learning and Others

2014-10-10 09:21

Instance Based Learning and Others

This is the learning as before, where given data inputs, make a function/model that generalize, map the output are.

Tools for Neural Networks & Others

2014-10-10 06:41

Tools for Neural Networks & Others

So the way gradient descent works is take a derrivative of the formula

Neural Networks & Perceptron

2014-10-10 05:35

Neural Networks & Perceptron

So we're creating truth table from this one.

Polynomial Regression

2014-10-09 06:04

Polynomial Regression

Inverse can't be implemented directly as X is not a squared matrix. That's way XtX to produce square matrix