dominodatalab/intro-to-machine-learning

Domino tutorial on the basics of Python and Machine Learning

Background

This notebook contains materials for the Introduction to Machine Learning workshop.

This data comes from a news article that used machine learning to identify potential spy aircraft over the continental United States. The data is available in this Github repository and on Kaggle and the code in this notebook is based on details found here.

Contents

The Demo-Notebooks folder contains the exercises for the Python overview, linear regression example, and logistic regression example.

The Spy-Plane-Project folder contains the following interactive activities:

• 1-SpyPlane-DataCleaning-Unsolved.ipynb
• 2-SpyPlane-RandomForestClassifier-Unsolved.ipynb
• 2-SpyPlane-RandomForestClassifier-Unsolved.ipynb
• 3-SpyPlane-EvaluatingModels-Unsolved.ipynb
• 4-SpyPlane-OptimizingModels-Unsolved.ipynb
• 5-SpyPlane-CreateModel.ipynb
• 6-SpyPlane-App-Visualizations.ipynb
• 7-SpyPlane-Flight-Path-Visualization.ipynb
• 8-SpyPlane-Feature-Engineering.ipynb
• Solved folder - contains solutions for each Unsolved notebook.

The Model-Comparison folder contains scripts to evaluate precision and recall for various models using the domino stats features in the Jobs overview page.

The Intro-to-ML-Reference.pdf is a reference guide to many of the functions needed for the intractive exercises.

Note that the hyperparameter tuning contest will not work outside of live Domino workshops unless you set up your own S3 bucket or other storage option.

Dependencies

When copying this project onto a new Domino instance, it may be necessary to adjust the Compute Environment. The following Domino-provided base image contains all dependencies for this project: quay.io/domino/base:Ubuntu18_DAD_Py3.6_R3.6_20190918.

These are the Pluggable Workspace settings for this base image:

jupyter:
  title: "Jupyter (Python, R, Julia)"
  iconUrl: "/assets/images/workspace-logos/Jupyter.svg"
  start: [ "/var/opt/workspaces/jupyter/start" ]
  httpProxy:
    port: 8888
    rewrite: false
    internalPath: "/{{ownerUsername}}/{{projectName}}/{{sessionPathComponent}}/{{runId}}/{{#if pathToOpen}}tree/{{pathToOpen}}{{/if}}"
    requireSubdomain: false
  supportedFileExtensions: [ ".ipynb" ]
jupyterlab:
  title: "JupyterLab"
  iconUrl: "/assets/images/workspace-logos/jupyterlab.svg"
  start: [  /var/opt/workspaces/Jupyterlab/start.sh ]
  httpProxy:
    internalPath: "/{{ownerUsername}}/{{projectName}}/{{sessionPathComponent}}/{{runId}}/{{#if pathToOpen}}tree/{{pathToOpen}}{{/if}}"
    port: 8888
    rewrite: false
    requireSubdomain: false
vscode:
 title: "vscode"
 iconUrl: "/assets/images/workspace-logos/vscode.svg"
 start: [ "/var/opt/workspaces/vscode/start" ]
 httpProxy:
    port: 8888
    requireSubdomain: false
rstudio:
  title: "RStudio"
  iconUrl: "/assets/images/workspace-logos/Rstudio.svg"
  start: [ "/var/opt/workspaces/rstudio/start" ]
  httpProxy:
    port: 8888
    requireSubdomain: false

See the Domino documentation for more details about managing compute environments. Additional package installations may be required if using other base images.