https://github.com/google/applied-machine-learning-intensive

Applied Machine Learning Intensive
https://github.com/google/applied-machine-learning-intensive

data-science machine-learning python3 sklearn tensorflow tensorflow-examples tensorflow-tutorials

Last synced: about 2 months ago
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Applied Machine Learning Intensive

• Host: GitHub
• URL: https://github.com/google/applied-machine-learning-intensive
• Owner: google
• License: apache-2.0
• Archived: true
• Created: 2019-08-27T20:32:26.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2021-04-06T06:31:55.000Z (over 3 years ago)
• Last Synced: 2024-09-23T06:33:15.491Z (about 2 months ago)
• Topics: data-science, machine-learning, python3, sklearn, tensorflow, tensorflow-examples, tensorflow-tutorials
• Language: Jupyter Notebook
• Homepage: https://github.com/google/applied-machine-learning-intensive
• Size: 62.8 MB
• Stars: 144
• Watchers: 10
• Forks: 68
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

• awesome-full-stack-machine-learning-courses - Google: Applied Machine Learning Intensive

README

        
# Applied Machine Learning Intensive

## Overview

The Applied Machine Learning Intensive (AMLI) is a collection of content

that can be used to teach machine learning. The original content was

created for a 10-week, bootcamp-style course for undergraduate college

students. Designed for students who weren’t necessarily majoring in

computer science, the goal was to enable participants to apply machine 

learning to different fields using high-level tools.

The content primarily consists of slides, [Jupyter](https://jupyter.org/)

notebooks, and facilitator guides. The slide decks are written in

[marp](https://marp.app/) markdown syntax, which can be exported to other

formats. The Jupyter notebooks were written in and targeted to run in

[Colab](https://colab.research.google.com/). The instructor guide as an

odt document.

## Answer Keys

Applied Machine Learning Intensive instructional materials are available

open source for faculty looking to run this program for students. This

repository offers all slide decks, facilitation guides, labs, and gradable

items. Because the program is considered academic in nature, we ask that

interested faculty fill out the form below to receive a password to unlock

the answer keys. We will provide you with a password that can be used to

unlock the keys using a standard zip program or the `tools/unlock_labs.py`

tool found in this repository.

Please fill out the following brief form to receive the answer keys for the curriculum:

https://docs.google.com/forms/d/e/1FAIpQLSd9v0az2wmKP659Xx5SlS7WPbQPD3u3yLXZMn0LHf3Vjj-ziw/viewform

The information that you submit will be maintained in accordance with

[Google’s Privacy Policy](http://www.google.com/policies/privacy/).

## Licensing Information

All course content (Colabs, slides, guides, and materials) are open sourced

under the [CC-BY-4.0 International license](https://creativecommons.org/licenses/by/4.0/).

All code contained in this course is open sourced under the

[Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).

Attribution and license information for content not created by Google will

be presented in the speaker notes.