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README.md

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 _For Octave/MatLab version of this repository please check [machine-learning-octave](https://github.com/trekhleb/machine-learning-octave) project._
 
-> This repository contains examples of popular machine learning algorithms implemented in **Python** with mathematics behind them being explained. Each algorithm has interactive **Jupyter Notebook** demo that allows you to play with training data, algorithms configurations and immediately see the results, charts and predictions **right in your browser**. In most cases the explanations are based on great [machine learning course](https://www.coursera.org/learn/machine-learning) by Andrew Ng.
+> This repository contains examples of popular machine learning algorithms implemented in **Python** with mathematics behind them being explained. Each algorithm has interactive **Jupyter Notebook** demo that allows you to play with training data, algorithms configurations and immediately see the results, charts and predictions **right in your browser**. In most cases the explanations are based on [this great machine learning course](https://www.coursera.org/learn/machine-learning) by Andrew Ng.
 
 The purpose of this repository is _not_ to implement machine learning algorithms by using 3<sup>rd</sup> 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.