A collection of commonly used machine learning algorithms implemented in Python/Numpy
This repository contains a collection of commonly used machine learning algorithms implemented in Python/Numpy
. No other third-party libraries (except Matplotlib
) are used.
This repository contains a collection of commonly used machine learning algorithms implemented in Python/Numpy
. No other third-party libraries (except Matplotlib
) are used.
Algorithms are implemented in Jupyter
notebooks. Before starting the coding section, we presented the basic intuition of the algorithm along with necessary mathematical derivations.
Optimized and computationally efficient algorithms were not our intention and we just wanted to produce an accessible collection of algorithms for students and software practitioner.
If you want to read Jupyter
notebooks just like static document, please follow the nbviewer
links or else to execute notebooks locally use the following instructions.
https://github.com/upul/Machine-Learning-Algorithms-From-Scratch.git
cd Machine-Learning-Algorithms-From-Scratch
jupyter notebook
In order to successfully following Jupyter
notebooks, we assume that you have a basic understanding of the following areas.
Following books were immensely helpful when we were preparing these Jupyter
notebooks. We believe these books should be available on every Machine Learning/Data Science practitioner's bookshelves.
Following MOOCs and Youtube playlists are simply amazing. If you want to broaden your Machine Learning knowledge I'm pretty sure those MOOCs and videos will be really helpful.