arset_ml_fundamentals

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

https://github.com/nasaarset/arset_ml_fundamentals

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Repository

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

Basic Info
  • Host: GitHub
  • Owner: NASAARSET
  • License: apache-2.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 13.1 MB
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  • Stars: 212
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Created over 3 years ago · Last pushed about 3 years ago
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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 | 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.

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 | 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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  • Login: NASAARSET
  • Kind: user

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