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Compare time series predictions of COVID-19 deaths(으)로 돌아가기

Coursera Project Network의 Compare time series predictions of COVID-19 deaths 학습자 리뷰 및 피드백

4.3
별점
23개의 평가
5개의 리뷰

강좌 소개

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....

최상위 리뷰

필터링 기준:

Compare time series predictions of COVID-19 deaths의 5개 리뷰 중 1~5

교육 기관: Brian U

2020년 11월 15일

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.

교육 기관: Sebastian D A

2021년 3월 7일

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!

교육 기관: Yanan Y

2021년 4월 8일

Excellent instructor!

교육 기관: Ramces M

2020년 10월 23일

informative

교육 기관: Celina S

2021년 6월 16일

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.