trekhleb/homemade-machine-learning

[](https://mybinder.org/v2/gh/trekhleb/homemade-machine-learning/master?filepath=notebooks) For Octave/MatLab version of this repository please check machine-learning-octave project. The purpose of this repository is not to implement machine learning algorithms by using 3rd party library one-liners but rather to practice implementing these algorithms from scratch and get better understanding of the mathematics behind each algorithm. That's why all algorithms implementations are called "homemade" and not intended to be used for production. In supervised learning we have a set of training data as an input and a set of labels or "correct answers" for each training set as an output. Then we're training our model (machine learning algorithm parameters) to map the input to the output correctly (to do correct prediction). The ultimate purpose is to find such model parameters that will successfully continue correct input→output mapping (predictions) even for new input examples. In regression problems we do real value predictions. Basically we try to draw a line/plane/n-dimensional…