https://github.com/glemaitre/dssp_06_2020

https://github.com/glemaitre/dssp_06_2020

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• Host: GitHub
• URL: https://github.com/glemaitre/dssp_06_2020
• Owner: glemaitre
• License: cc0-1.0
• Created: 2020-06-10T08:16:44.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-06-10T08:17:33.000Z (over 4 years ago)
• Last Synced: 2024-10-28T02:06:15.966Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 5.26 MB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# DSSP 14

## Curriculum

This lecture is focused on the following concepts:

1. Introduction the Python programming language;

2. Data wrangling using Pandas;

3. Applied mathematics using NumPy;

4. Understand linear models;

5. Understand tree-based algorithms;

6. Manage mixed data types in machine-learning pipeline;

7. Fine tuning model by hyper-parameters search.

## Additional material:

Some intro slides: http://ogrisel.github.io/decks/2017_intro_sklearn

## Getting started

In case that you have any issues, you click on the binder link below

which will setup an online machine for you:

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/glemaitre/dssp_12_2019/master)

Alternatively you can create a new conda environment which will be called

`dssp` by default and whill contain all the packages required to run the

notebooks:

``` bash

conda env create -f environment.yml

conda activate dssp

```

```bash

cd path/to/dssp_12_2019

jupyter notebook

```

You can also update an existing `conda` environment:

``` bash

conda env update -f environment.yml

```

## References

This material is inspired and reused part of the following materials:

* https://github.com/amueller/scipy-2018-sklearn

* https://github.com/lesteve/euroscipy-2019-scikit-learn-tutorial