https://github.com/carpentries-incubator/machine-learning-novice-sklearn

A Carpentry style lesson on machine learning with Python and scikit-learn.
https://github.com/carpentries-incubator/machine-learning-novice-sklearn

alpha carpentries-incubator english lesson machine-learning python sklearn

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A Carpentry style lesson on machine learning with Python and scikit-learn.

• Host: GitHub
• URL: https://github.com/carpentries-incubator/machine-learning-novice-sklearn
• Owner: carpentries-incubator
• License: other
• Created: 2019-06-27T14:15:12.000Z (over 6 years ago)
• Default Branch: gh-pages
• Last Pushed: 2024-12-13T17:44:57.000Z (10 months ago)
• Last Synced: 2024-12-13T18:35:16.245Z (10 months ago)
• Topics: alpha, carpentries-incubator, english, lesson, machine-learning, python, sklearn
• Language: Jupyter Notebook
• Homepage: https://carpentries-incubator.github.io/machine-learning-novice-sklearn/
• Size: 23.1 MB
• Stars: 28
• Watchers: 10
• Forks: 33
• Open Issues: 24
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION
  • Authors: AUTHORS

Awesome Lists containing this project

README

          
# Introduction to Machine Learning with Scikit Learn and Python

[![Create a Slack Account with us](https://img.shields.io/badge/Create_Slack_Account-The_Carpentries-071159.svg)](https://swc-slack-invite.herokuapp.com/)

This repository generates the corresponding lesson website from [The Carpentries](https://carpentries.org/) repertoire of lessons. 

## Contributing

We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any

questions, concerns, or experience any difficulties along the way.

We'd like to ask you to familiarize yourself with our [Contribution Guide](CONTRIBUTING.md) and have a look at

the [more detailed guidelines][lesson-example] on proper formatting, ways to render the lesson locally, and even

how to write new episodes.

Please see the current list of [issues](https://github.com/carpentries-incubator/machine-learning-novice-sklearn/issues) for ideas for contributing to this

repository. For making your contribution, we use the GitHub flow, which is

nicely explained in the chapter [Contributing to a Project](http://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project) in Pro Git

by Scott Chacon.

Look for the tag ![good_first_issue](https://img.shields.io/badge/-good%20first%20issue-gold.svg). This indicates that the mantainers will welcome a pull request fixing this issue.  

## Maintainer(s)

Current maintainers of this lesson are:

* [Colin Sauze](https://github.com/colinsauze)

* [Vini Salazar](https://github.com/vinisalazar)

## Outline

As determined by the attendees of CarpentryConnect Manchester 2019, the proposed outline of this lesson is as follows:

### Unsupervised Learning

#### I. Clustering

##### 1. Kmeans

#### II. Dimesionality Reduction

##### 1. PCA

##### 2. TSNE

### Supervised Learning

All models, objectives:

- What it is;

- when to use it and on what type of data;

- how to evaluate the fit, over/underfitting;

- computational complexity

#### I. Regression

##### 1. Linear

##### 2. Polynomial

- Overfitting/underfitting

- Test sets (how and why)

#### II. Classification

##### 1. Logistic regression

- Over/underfitting can happen in regression too

- Accuracy

- Confusion Matrix

- Precision

- Recall

##### 2. Random Forest

##### 3. Neural Networks

- Evaluation

- Cross Validation

### Ethics

## Authors

A list of contributors to the lesson can be found in [AUTHORS](AUTHORS)

## Citation

To cite this lesson, please consult with [CITATION](CITATION)

[lesson-example]: https://carpentries.github.io/lesson-example