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Repository for Jupyter Notebook examples associated with the NASA ARSET Training, "Fundamentals of Machine Learning for Earth Science"

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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 Open In Colab
Assignment Session 1 Open In Colab

Session 2 Materials:

Lecture Topic Interactive Link
MODIS EDA Open In Colab
MODIS Train & Eval Open In Colab
Assignment Session 2 Open In Colab

Session 3 Materials:

Lecture Topic Interactive Link
MODIS Model Tuning Open In Colab
MODIS Explainability Open In Colab
MODIS AutoML Open In Colab
Assignment Session 3 Open In Colab

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.

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

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 Open In Colab
Introduction to Git Open In Colab

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):

Python Assessment Test

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 Open In Colab
Data Types Open In Colab
Conditional Statements Open In Colab
Loops Open In Colab
Advanced Data Types Open In Colab
Functions Open In Colab
Modules Open In Colab
I/O on Text Files Open In Colab
Lecture Topic Interactive Link
Introduction to Turtle Open In Colab
A place to run the code https://repl.it/

Data Science Tools

Lecture Topic Interactive Link
Introduction to Numpy Open In Colab
Basic Visualization with Matplotlib Open In Colab
Introduction to Pandas Open In Colab

Additional References

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