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This repo contains the workshop material for [The Hacker Within Berkeley](http://www.thehackerwithin.org/berkeley/) Fall 2017, you can find more information [here](http://www.thehackerwithin.org/berkeley/posts/2017-12-06-sklearn-f17.html). It is a short version of the material Qingkai taught at [2017 CDIPS Data Science Workshop](http://cdips.physics.berkeley.edu/2017-cdips-data-science-workshop/), you can find the long version [here](https://github.com/qingkaikong/20170628_ML_sklearn). It is developed by [Qingkai Kong](http://seismo.berkeley.edu/qingkaikong/). 

## Goals of the workshop

* Understand the basics of Machine Learning, only classification and regression
* Get familiar with the syntax of scikit-learn

After the workshop, you should be able to use popular models in your problems. 

## The setup

This workshop will use Python 3. If your main environment is Python 2, you can create a virtual environment to use Python 3 without messing up with your existing environment. You can follow steps [here](http://qingkaikong.blogspot.com/2017/06/python-using-virtual-environments.html) 

The easiest way to get these working is to use the [conda](http://store.continuum.io/) environment manager.
I suggest downloading and installing [miniconda](http://conda.pydata.org/miniconda.html).

The following command will install all required packages:

```
$ pip install -r requirements.txt
```

## References

* [Intro-to-sklearn](https://github.com/PythonWorkshop/intro-to-sklearn)
* [sklearn tutorial](https://github.com/jakevdp/sklearn_tutorial) by [Jake Vanderplas](https://staff.washington.edu/jakevdp/)
* [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/) by [Sebastian Raschka](https://sebastianraschka.com/)
* [sklearn documentation](http://scikit-learn.org/stable/documentation.html)  
* [sklearn examples](http://scikit-learn.org/stable/auto_examples/index.html)   
