This is a collection of scripts (implemented in Octave and python (vanilla, numpy and/or matplotlib)) Everything is well documented, and has sample test data to try out the scrips with. The scripts range from supervised to unsupervised, covering everything from simple gradient descent to more complex neural network implemetations. Furthermore, some scripts are written in both numpy as well as Vanilla python to support cross compatibility.
- Every directory contains a relevant
*.mdfile that contains the instructions to run the scripts in that file. - Most scripts come with a
dataset generation script, that will help you to generate a random dataset for the specific algortihm. - In addition to this, a few predefined datasets have also been included.
- All code is well documented and commented for easy readability and understanding.
- Linux preferred with python 3.x support. (Numpy and matplotlib are essential for a few scripts)
- This folder contains all the assignments and exercises covered by the Machine learning course by Andrew Ng on Coursera. They have pfs as well as the solutions for every exercise.
- You can use Octave OR Matlab to implement the exercises given.
- 0Octave
- machine-learning-ex8
- machine-learning-ex7
- machine-learning-ex6
- machine-learning-ex5
- machine-learning-ex4
- machine-learning-ex3
- machine-learning-ex2
- machine-learning-ex1
- 1Scripts