https://github.com/wardlt/machine-learning-intro
Notebooks used to introduce scientists to machine learning
https://github.com/wardlt/machine-learning-intro
Last synced: 3 months ago
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Notebooks used to introduce scientists to machine learning
- Host: GitHub
- URL: https://github.com/wardlt/machine-learning-intro
- Owner: WardLT
- Created: 2021-12-16T12:21:27.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-12-16T14:02:34.000Z (over 4 years ago)
- Last Synced: 2025-03-05T22:44:49.110Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 15.1 MB
- Stars: 1
- Watchers: 0
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Introduction to Machine Learning
[](https://mybinder.org/v2/gh/WardLT/machine-learning-intro/HEAD)
The corresponding notebooks to the lecture focus on first learning how to use Scikit-Learn, a widely-used machine learning package in Python,
and then illustrate how to use it with scientific data that requires pre-processing, using molecular property prediction as an example.
This is a standalone version of the machine learning tutorial from the [ALCF AI Training Series](https://github.com/argonne-lcf/ai-science-training-series/tree/main/01_machineLearning).
## Environment Setup
There are two ways to run the notebooks.
### Binder
Binder will build the enviornment for you and host it on a cloud-hosted instance. Just click: [](https://mybinder.org/v2/gh/WardLT/machine-learning-intro/HEAD)
### Local Installation
The `environment.yml` file provided with this README describes how to build the environment with anaconda.
Once you have anaconda installed, build the environment by calling:
```bash
conda env create --file environment.yml
```
from the command line. Once installed, follow the instructions Anaconda generates to activate the environment and then launch Jupyter:
```bash
jupyter lab
```