Working an example of supervised machine learning in Python
Supporting material for workshop on beginning data science in Python. Used as follow-up material to lecture. A short video on how to install the required software on a Mac can be found here.
Course purpose:
- An overview, followed by a vertical deep dive into what data science is like and how data scientists work
- This can serve as an introduction to data science tools and tasks, or as a guide how to provision for and work with data scientists and data science tools
- Or: give a team days of data science tools experience in hours, by using guided examples
Our exercises: on the laptop
- Small scale idealized examples on your machine in Python using the CPU (not a GPU or a TPU)
- Link: https://github.com/WinVector/ATasteOfDataScience , however this is intended for a live guided session (with detialed slides/hand-outs)
- Goal: play with the code and concepts
- An experience you learn from, and move on from
Industry practice: in the factory
- Large scale examples only runnable in production systems including TPUs and remote services / storage buckets
- Examples: https://www.fast.ai , https://www.kaggle.com , https://www.tensorflow.org/hub
- Goal: operational experience in target systems
- An environment to work in
"Plan to throw one away" -- Fred Brooks, the Mythical Man-Month
"If you want to build a ship, don't drum up people to collect wood and don't assign them tasks and work, but rather teach them to long for the endless immensity of the sea." -- Antoine de Saint Exupéry
To arrange sessions (either in person on by teleconference) please reach out to John Mount at [email protected]
Some details on installing can be found here.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
