Here you can find all my programming assignments (in Octave along with some comments to help) from the [2015-16 Coursera Machine Learning Class] (https://www.coursera.org/learn/machine-learning) and additionally a Python version of all the programming assignments.. If you are taking the class, fork my repo and puth different solutions in a different branch. HOWEVER, please DO NOT refer any code in my repo before the due date and NEVER post any code in my repo according to "Stanford Honer Code" below. Questions or pointing out is always welcome, please send me a mail. Finally, in order to really benefit from the course I strongely encourage people to take a look at the other files of the p-assignments so as to have a grasp of a ML system sequence. Enjoy :)

"We strongly encourage students to form study groups, and discuss the lecture videos (including in-video questions). We also encourage you to get together with friends to watch the videos together as a group. However, the answers that you submit for the review questions should be your own work. For the programming exercises, you are welcome to discuss them with other students, discuss specific algorithms, properties of algorithms, etc.; we ask only that you not look at any source code written by a different student, nor show your solution code to other students."

[Octave] (https://www.gnu.org/software/octave/) is a high level (open source) programming language similar to Matlab. I'm using it for the 2015-16 Stanford Machine Learning Class.

All Solutions licensed under MIT License. See LICENSE.txt for further details.

Copyright (c) 2015 [jrbadiabo] (https://github.com/jrbadiabo).