Meeshkan: Machine Learning the GitHub API

Learn how to plan, deploy and run a Machine Learning problem on AWS and Meeshkan
Free tutorial
Rating: 4.4 out of 5 (89 ratings)
18,826 students
1hr 27min of on-demand video

Collect and analyze a large dataset using Meeshkan.


  • You should have basic familiarity with Machine Learning. JavaScript knowledge is a plus!


In this course, Meeshkan C.E.O. Mike Solomon will teach you how to do Machine Learning on Meeshkan.

Meeshkan is an easy and inexpensive platform where people can explore ideas in AI, Machine Learning and Deep Learning.

This course starts with a simple AI question: can a machine predict if a GitHub project will be successful by analyzing only the first few commits of that project?

The first section of the course will run the Machine Learning project on Meeshkan.  You'll see how quick and easy it is to do Machine Learning on Meeshkan.

The second section of the course will delve into each step of the process in detail, covering data collection, data egress, infrastructure deployment, model design, model executing and result analysis.

By the end of the course, you will be able to adapt the course materials to design, run, and explore your own Machine Learning models using public APIs and the Meeshkan Machine Learning service.

Who this course is for:

  • Anyone who wants to learn about Machine Learning.


C.E.O. Meeshkan Machine Learning
Mike Solomon
  • 4.4 Instructor Rating
  • 142 Reviews
  • 34,174 Students
  • 2 Courses

Mike Solomon is the lead developer of Wags, a browser-based DAW and music sharing platform

Mike is a graduate of Stanford University, Queen's Belfast, the University of Florida and UPMC in Paris.

Before hacking on wags, he worked in the IRCAM, Jongla and Grame doing Computer Music, Audio and AI research and development.

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