https://github.com/leouieda/ml-intro

A very brief introduction to machine learning
https://github.com/leouieda/ml-intro

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A very brief introduction to machine learning

• Host: GitHub
• URL: https://github.com/leouieda/ml-intro
• Owner: leouieda
• License: other
• Created: 2021-12-03T05:38:24.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-11-18T16:09:16.000Z (about 2 years ago)
• Last Synced: 2024-10-04T23:30:51.569Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 6.91 MB
• Stars: 39
• Watchers: 3
• Forks: 2
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# A quick introduction to machine learning

**Author:** [Leonardo Uieda](https://www.leouieda.com/)

    

This is a very brief hands-on introduction to machine learning. 

It will cover some of the common nomenclature, principles, and applications. 

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/leouieda/ml-intro/HEAD?labpath=tutorial.ipynb)

## 📓 • Jupyter Notebook

The tutorial is in the form of a Jupyter notebook (`tutorial.ipynb`). 

Here are some options for using it:

* [Download the notebook](https://github.com/leouieda/ml-intro/archive/refs/heads/main.zip) and run it on your machine (**preferred**).

* [Run it online on Binder](https://mybinder.org/v2/gh/leouieda/ml-intro/HEAD?labpath=tutorial.ipynb) which lets you try out the code and experiment but will **not save your progress**.

* [View it online on nbviewer](https://nbviewer.org/github/leouieda/ml-intro/blob/main/tutorial.ipynb) to read the text and look at the code but not run it.

## 🧑🏿‍💻 • Learner profile

* Is currently in their final year of a STEM undergraduate degree or early years of a postgraduate degree.

* Has studies the basics of statistics, Python programming, and linear algebra.

* Is interested in using machine learning in their projects or as a future career.

## 🧑‍🏫 • For instructors

The tutorial is designed to be taught as a 1-2 hour session with **live-coding**. 

To do so, create a copy of the notebook and delete all or most of the code cells 

(it's OK to leave some in to allow more time in the tutorial). 

Type in the code as you explain what you're doing. 

This will help you control your pacing and avoid going too fast. 

It also opens up the opportunity for you to make mistakes and teach students 

how to identify and solve them.

Ideally, have them follow along on their own computers, typing in the code with you.

Make sure you also share a copy of the pre-filled notebook with students so that 

they can choose to not type and listen at the same time.

## ⚖️ • License

The original material for this tutorial can be found at [leouieda/ml-intro](https://github.com/leouieda/ml-intro).

Comments, corrections, and additions are welcome.

All Python source code is made available under the BSD 3-clause license. You

can freely use and modify the code, without warranty, so long as you provide

attribution to the authors.

Unless otherwise specified, all figures and Jupyter notebooks are available

under the [Creative Commons Attribution 4.0 License (CC-BY)](https://creativecommons.org/licenses/by/4.0/).

The full text of these licenses is provided in the [`LICENSE.txt`](LICENSE.txt)

file.