Handling Sensitive Data

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prerequisites
intermediate Python • basics of data science • basics of machine learning
skills learned
basic flow control in Python • create and manipulate figures in Python • create and manipulate NumPy arrays to represent matrices and vectors
Alexander Jung
1 week · 6-8 hours per week · ADVANCED
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In this liveProject, you’ll develop a machine learning model that can make a personalized prediction of whether an individual has COVID-19. You’ll work with a fictional audio dataset of digital footprints from smart phones, applying logistic regression and decision trees to turn this dataset into a COVID-19 predictor. You’ll learn how to formulate a ML problem by identifying data points, and their features and labels, so that you can take advantage of ready-made ML methods.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Alexander Jung
Alexander Jung is an assistant professor for machine learning at Aalto University in Finland. Prior to joining Aalto, he obtained a PhD in statistical signal processing from TU Vienna in 2012 and was a postdoc at TU Vienna and ETH Zurich. Alex leads the Aalto research group “Machine Learning for Big Data'' that studies the fundamental limits and efficient algorithms for machine learning from large distributed collections of data. His current research focus is on privacy preserving and explainable federated machine learning methods for big data over networks. Alex has developed some of the most popular courses at Aalto University. He was selected as the Teacher of the Year by the Department of Computer Science in 2018.

prerequisites

This liveProject is for Python data scientists interested in applying big data analytics to public healthcare. To begin this liveProject you will need to be familiar with the following:

TOOLS
  • Intermediate Python (declaring variables, loops, branches, debugging, importing modules)
  • Basics of Matplotlib
  • Basics of NumPy
TECHNIQUES
  • Basics of data science
  • Basics of machine learning (logistic regression, decision tree methods of scikit-learn)

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