First Kaggle Competition

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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.

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Boosting, Post-decessor

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.
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Support Vector Machines

Support Vector Machines


Boosting

Boosting
  • So instead of taking uniformly randomly subsets of data, take "hardest examples" from it
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Boosting, Pre-decesor

Boosting, Pre-decesor

  • Now we're implementing something called spam classfier.
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kNN

kNN

  • So this is the kNN algorithm that based on the housing prices example
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Instance Based Learning and Others

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.
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Tools for Neural Networks & Others

Tools for Neural Networks & Others

  • So the way gradient descent works is take a derrivative of the formula
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Neural Networks & Perceptron

Neural Networks & Perceptron




Polynomial Regression

Polynomial Regression


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