###Python adaptation of the coursera's Machine Learning course taught by Professa Andrew Ng### This is the python adaptation of the machine learning course taught by professa Andrew Ng . The codes are translated by Bill Williams and are made to replicate the functionality of the ones provided by coursera and written in octave\Matlab ###NOTE###
- The codes contained are not in any way affiliated to coursera and neither is the author/programmer a coursera staff nor working for coursera
- Python 2.7.6 was used during the creation of the scripts.Generally this should be compatible with any python 2.7.x but cant guarantee functionality in python 3.x.x
- Numpy version 1.8.1 was used during the code creation Intro
The following python tools and modules are recquired to replicate the Octave\Matlab functionality
- Numpy (Numerical Python) : A python module used for linear algebra computations
- Pylab : A python plotting module to help with 2d plotting
- Ipython : An interactive python shell that has way more functionality than the standard python shell and is great while executing scripts
###Contents### ex1-005
This contains the codes for the first programming exercise which involves linear regression with single and multiple variables the README file in the ex1-005 folder contains instructions on how to run the code
ex2-005
This contains the codes for the second programming exercise which involves logistic regression with and without regularization the README file in the ex2-005 folder contains instructions on how to run the code
ex3-005
This contains the codes for the third programming exercise which involves logistic regression and neural network learning for handwritten digit recognition the README file in the ex3-005 folder contains instructions on how to run the code