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Bewertung und Feedback des Lernenden für Graduate Admission Prediction with Pyspark ML von Coursera Project Network

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6 Bewertungen

Über den Kurs

In this 1 hour long project-based course, you will learn to build a linear regression model using Pyspark ML to predict students' admission at the university. We will use the graduate admission 2 data set from Kaggle. Our goal is to use a Simple Linear Regression Machine Learning Algorithm from the Pyspark Machine learning library to predict the chances of getting admission. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for the learning purposes. By the end of this project, you will be able to build the linear regression model using Pyspark ML to predict admission chances.You will also be able to setup and work with Pyspark on the Google Colab environment. Additionally, you will also be able to clean and prepare data for analysis. You should be familiar with the Python Programming language and you should have a theoretical understanding of Linear Regression algorithm. 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 - 6 von 6 Bewertungen für Graduate Admission Prediction with Pyspark ML

von Cheikh B

13. Mai 2021

Great project very clear and easy to understand. Thank you for this great project i hope you will make the same project for regression, deeplearning in pyspark.

Thank tou Coursera

von Feng J

16. Mai 2021

This class is explained very clearly, so that I could understand how to use pyspark completely. Thank you so much for teaching us in such a great way !

von Alexandra A

26. Aug. 2021

Straightforward tutorial of how to use pyspark for a simple machine learning task.

von Carlos A P

25. Okt. 2020

Good taste for PySpark ML

von Muhammad M

25. Dez. 2020

very informative

von Aruparna M

31. Jan. 2021

More details were required.