MIT

This repository contains popular Machine Learning algorithms, which have been introduced in various blog posts (http://ataspinar.com). Most of the algorithms are accompanied with blog-posts in which I try to explain the mathematics behind and the interpretation of these algorithms.

Machine Learning is fun! But more importantly, Machine Learning is easy. But the academic literature or even (wikipedia-pages) is full with unnecessary complicated terminology, notation and formulae. This gives people the idea that these ML algorithms can only be understood with a full understanding of advanced math and statistics. Stripped from all of these superfluous language we are left with simple maths which can be expressed in a few lines of code.

I have also provided some notebooks, explaining the mathematics of some Machine Learning algorithms.

- Introduction to PyWavelets (for Wavelet Analysis
- Using Wavelets to Visualize the Scaleogram, time-axis and Fourier Transform
- Classification of signals using the Continuous Wavelet Transform and Convolutional Neural Networks
- Classification of ECG signals using the Discrete Wavelet Transform and Gradient Boosting
- Classification of signals using the Discrete Wavelet Transform and several classifiers

To install **siML**:

```
(sudo) pip install siml
```

or you can clone the repository and in the folder containing setup.py

```
python setup.py install
```

TODO

Great Documentation0

Easy to Use0

Performant0

Highly Customizable0

Bleeding Edge0

Responsive Maintainers0

Poor Documentation0

Hard to Use0

Slow0

Buggy0

Abandoned0

Unwelcoming Community0