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.

• Linear Regression and Logistic Regression

• Naive Bayes Classification

• Perceptron Classification

• Classification with Scikit-Learn

• Machine Learning with Signal Analysis techniques

• 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