# First Kaggle Competition

This Tutorial is from:

![] (/galleries/Kaggle1st/1.jpg, raw = true)

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

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

# Boosting

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

# 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

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

# Polynomial Regression

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