[Free] Differentially Private Python Web Applications
Learn how to deploy differentially private machine learning solutions in the matter of hours. – Free Course
What you’ll learn
- How to create differentially private (perserving information privacy) python web applications
- How to quickly iterate and create minimum viable product (MVP)
- How to embedd machine learning solutions in order to preserve privacy
Requirements
- Python
- Data Science
- Machine Learning
Description
Keeping it short and sweet, we will be focusing on these two topics:1. How to deploy python web-apps in matter of hours, i.e. create fastly your minimum viable product MVP
2. Whats differential privacy, how does it work, how can we use it off the shelf and incroporate it in our machine learning solution?
We will be moving away from the jupyter-like development enviroment and start serving applications to the consumer.
Three aplications:
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Titanic challenge, the famous kaggle challenge will be served and start beeing accessible as a differentially private machine learning solution
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Road trip app, highly versatile app where we try to predict some event (in my case wether the road trip will take place or not) given the weather forecast. But you can use my code from github and modify it to your organisations variant.
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Corona webapp. Given the new situation with this pandemic, we showcased how can you create a product (mortality application) very quickly, and adjust and potentially help people.
