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Learner Reviews & Feedback for Compare time series predictions of COVID-19 deaths by Coursera Project Network

4.4
stars
20 ratings
5 reviews

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....

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1 - 5 of 5 Reviews for Compare time series predictions of COVID-19 deaths

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.

By Sebastian D A

Mar 7, 2021

Very complete for a small 2 hour project! But Please write some parts of the code on the next project, because the pace is too fast, and the notebooks are empty!

By Yanan Y

Apr 8, 2021

Excellent instructor!

By Richard A M

Oct 23, 2020

informative

By Celina S

Jun 16, 2021

It explains well how to prepare data, create models, train them and evaluate them in the test set. Sadly, it does not explain how to retrain the models and forecast for future dates instead of just the test set, which I think it's the most challenging part.