Skip to content

7-gate-academy-ml-program/Synopsis

 
 

Repository files navigation

7 Gate ML Cohort Synopsis

This repository is storing the progress the 7 Gate Machine Learning Cohort students made through their program. The content is served as a webpage.

Add yourself as a student

  1. Fork the repo

  2. Add an .md file for your profile under _students, following the naming convention (lastname_firstname.md)

  3. Add the "front matter", i.e. the part that looks like below. Be sure it starts and ends exactly with the three hyphens ---, and that the rest is key value pairs with the appropriate syntax.

    ---
    name: Example Student
    resume: true
    ---
    

    Set the resume to true or false depending on wether you want to have a resume shown or not.

  4. Add a subdirectory that has the same name as your .md file (i.e. lastname_firstname). In that subdirectory, put your profile picture exactly as profile.jpg (note the lower-case and the jpg extension). Try to keep the image size less than a MB. The photo must be square shaped (1:1 aspect ratio).

  5. If you have set resume: true put your resume in the same place as resume.pdf.

  6. Do a pull request to incorporate your bio into the site.

Submit your first homework

When you submit your homeworks to Synopsis, people who visit your profile can see them.

  1. Pull the latest changes.
  2. Inside your own folder, make a new folder named homework1. For subsequent homeworks, name the folder accordingly homework2, homework3, ...
  3. Inside the folder put an index.pdf (or index.html if the homework asks for it). This is the presentations of your homework, it can be an export of your python notebook for example.
  4. Feel free to add any supplementary material such as the .ipynb file (must be index.ipynb). Be mindful of the size of the files (no ML models or data etc.)
  5. Submit a pull request.

About

Course Synopsis for 7 Gate Academy ML Cohort

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 98.4%
  • HTML 1.1%
  • Ruby 0.2%
  • Python 0.2%
  • CSS 0.1%
  • JavaScript 0.0%