ARSET Fundamentals of Machine Learning for Earth Science
This training will provide attendees an overview of machine learning in regards to Earth Science, and how to apply these algorithms and techniques to remote sensing data in a meaningful way.
https://github.com/nasaarset/arset_ml_fundamentals
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Repository metadata
Repository for Jupyter Notebook examples associated with the NASA ARSET Training, "Fundamentals of Machine Learning for Earth Science"
- Host: GitHub
- URL: https://github.com/nasaarset/arset_ml_fundamentals
- Owner: NASAARSET
- License: apache-2.0
- Created: 2023-03-28T21:07:02.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-05-04T17:54:35.000Z (almost 2 years ago)
- Last Synced: 2025-04-23T10:01:49.561Z (4 days ago)
- Language: Jupyter Notebook
- Size: 13.1 MB
- Stars: 205
- Watchers: 13
- Forks: 133
- Open Issues: 0
- Releases: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
ARSET Fundamentals of Machine Learning (ML) for Earth Science
Materials for ARSET Fundamentals of Machine Learning for Earth Science. This repository contains materials for Session 1, 2, and 3.
Assignments
The assignments listed for each session are practice assignments with questions that will be included in the final assignment after Session 3 conclusion.
The final assignment will be through a Google Form where you will be answering a set of questions from each one of the Sessions.
Session 1 Materials:
| Lecture Topic | Interactive Link |
|---|---|
| ML Algorithms Introduction |  |
| Assignment Session 1 | |
Session 2 Materials:
| Lecture Topic | Interactive Link |
|---|---|
| MODIS EDA | |
| MODIS Train & Eval | |
| Assignment Session 2 | |
Session 3 Materials:
| Lecture Topic | Interactive Link |
|---|---|
| MODIS Model Tuning | |
| MODIS Explainability | |
| MODIS AutoML | |
| Assignment Session 3 | |
Additional Resources
The NASA ASTG provides additional introductory materials related to Python and
data science in general. You can access some of this interactive material directly from their repository NASA ASTG py_materials or under the links below.
Installing the Anaconda Python Distribution
It is not required to have a Python distribution installed on your local machine.
However, we believe that it is important to have one in order to write and run your own Python
applications. We recommend that you install
the Anaconda Python distribution by following the instructions at: Anconda installation Guide
Installing Git
To install Git on your local machine, follow the installation instructions: Getting Started - Installing Git
To fully follow all the topics below, you need to have a gmail account in order to access Google Colaboratory. Each course will be taught through the Google cloud based Jupyter notebook.
Starting Point
| Lecture Topic | Interactive Link |
|---|---|
| Introduction to Jupyter Notebook | |
| Introduction to Git | |
Introduction to Python
If you have never been exposed to Python, you need to take this Introduction to Python course. In case you did some Python programming in the past and you want to assess your Python knowledge, take the following test (in less that 15 minutes and without using any help):
If you score at least 80% then only take the I/O on Text Files topic. Otherwise, take the entire course.
| Lecture Topic | Interactive Link |
|---|---|
| Running Python | |
| Data Types | |
| Conditional Statements | |
| Loops | |
| Advanced Data Types | |
| Functions | |
| Modules | |
| I/O on Text Files | |
| Lecture Topic | Interactive Link |
|---|---|
| Introduction to Turtle | |
| A place to run the code | https://repl.it/ |
Data Science Tools
| Lecture Topic | Interactive Link |
|---|---|
| Introduction to Numpy | |
| Basic Visualization with Matplotlib | |
| Introduction to Pandas | |
Additional References
Owner metadata
- Name:
- Login: NASAARSET
- Email:
- Kind: user
- Description:
- Website: https://appliedsciences.nasa.gov/arset
- Location:
- Twitter: NASAARSET
- Company:
- Icon url: https://avatars.githubusercontent.com/u/69639909?u=4feb8b9a35524410ee2475a4ab1ea9669553ce28&v=4
- Repositories: 2
- Last ynced at: 2023-04-20T16:21:02.721Z
- Profile URL: https://github.com/NASAARSET
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Last synced: 5 days ago
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Development Distribution Score (DDS): 0.108
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| Name | Commits | |
|---|---|---|
| Jordan A Caraballo-Vega | j****a@n****v | 33 |
| Caleb Spradlin | c****n@n****v | 4 |
Committer domains:
- nasa.gov: 2
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