<commit_before># Machine Learning From Scratch

## About
Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.

The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible 
but rather to present the inner workings of them in a transparent way.
The reason the project uses scikit-learn is to evaluate the implementations on sklearn.datasets.

Feel free to [reach out](mailto:<EMAIL>) if you can think of ways to expand this project.

## Table of Contents
- [Machine Learning From Scratch](#machine-learning-from-scratch)
  * [About](#about)
  * [Table of Contents](#table-of-contents)
  * [Usage](#usage)
    + [Installation](#installation)
    + [Regression](#regression)
    + [Classification](#classification)
    + [Clustering](#clustering)
    + [Association Analysis](#association-analysis)
  * [Current Implementations](#current-implementations)
    + [Supervised Learning](#supervised-learning)
    + [Unsupervised Learning](#unsupervised-learning)
    
## Usage
### Installation
    $ pip install mlfromscratch

or

    $ python setup.py install


### Regression
    $ python mlfromscratch/supervised_learning/regression.py

<p align="center">
    <img src="http://eriklindernoren.se/images/prr.png" width="640"\>
</p>
<p align="center">
    Figure: Polynomial ridge regression of temperature data measured in Linköping,<br>
    Sweden 2016.
</p>
<commit_msg>README merge confl.<commit_after># Machine Learning From Scratch

## About
Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.

The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible 
but rather to present the inner workings of them in a transparent way.
The reason the project uses scikit-learn is to evaluate the implementations on sklearn.datasets.

Feel free to [reach out](mailto:<EMAIL>) if you can think of ways to expand this project.

## Table of Contents
- [Machine Learning From Scratch](#machine-learning-from-scratch)
  * [About](#about)
  * [Table of Contents](#table-of-contents)
  * [Usage](#usage)
    + [Installation](#installation)
    + [Regression](#regression)
    + [Classification](#classification)
    + [Clustering](#clustering)
    + [Association Analysis](#association-analysis)
  * [Current Implementations](#current-implementations)
    + [Supervised Learning](#supervised-learning)
    + [Unsupervised Learning](#unsupervised-learning)
    
## Usage
### Installation
    $ pip install mlfromscratch

or

    $ python setup.py install


### Regression
    $ python mlfromscratch/supervised_learning/regression.py

<p align="center">
    <img src="http://eriklindernoren.se/images/prr.png" width="640"\>
</p>
<p align="center">
    Figure: Polynomial ridge regression of temperature data measured in <br> 
    Linköping, Sweden 2016.
</p>

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