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.
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:
- Intermediate Python (declaring variables, loops, branches, debugging, importing modules)
- Basics of Matplotlib
- Basics of NumPy
- 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