Federated Learning

Handling Sensitive Data you own this product

This project is part of the liveProject series Federated Learning Over Networks for Pandemics
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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liveProject This project is part of the liveProject series Federated Learning Over Networks for Pandemics liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save: $10 (33%)
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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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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)

you will learn

In this liveProject, you’ll learn how to formulate a machine learning data problem for an accurate solution. Correctly defining data problems is one of the big challenges of machine learning, with the subsequent solution taking much less time!

  • Basic flow control in Python
  • Create and manipulate figures in Python
  • Create and manipulate NumPyarrays to represent matrices and vectors

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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