Back to Compare time series predictions of COVID-19 deaths
Learner Reviews & Feedback for Compare time series predictions of COVID-19 deaths by Coursera Project Network
13 ratings
About the Course
In this 2-hour long project-based course, you will learn how to preprocess time series data, visualize time series data and compare the time series predictions of 4 machine learning models.You will create time series analysis models in the python programming language to predict the daily deaths due to SARS-CoV-19, or COVID-19. You will create and train the following models: SARIMAX, Prophet, neural networks and XGBOOST. You will visualize data using the matplotlib library, and extract features from a time series data set, perform data splitting and normalization.
To successfully complete this project, learners should have prior Python programming experience, a basic understanding of machine learning, and a familiarity of the Pandas library.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Filter by:
1 - 2 of 2 Reviews for Compare time series predictions of COVID-19 deaths
By Richard A M
•Oct 23, 2020
informative
By Brian U
•Nov 15, 2020
Perhaps it's not fair to compare this to full Coursera courses I have taken in the past, but I was disappointed that the built-in Colab notebook was clunky and there was a time limit on using it! I realized later that I could go to the resources section and download a .ipynb file to use in my own jupyter notebook. That made a huge difference! Otherwise, the course gave examples of how to use the four ML libraries and I was able to fill in some of the details afterwards.
Coursera Footer
