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Graduate Admission Prediction with Pyspark ML(으)로 돌아가기

Coursera Project Network의 Graduate Admission Prediction with Pyspark ML 학습자 리뷰 및 피드백

4.7
별점
24개의 평가

강좌 소개

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....

최상위 리뷰

AA

2021년 8월 25일

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

CJ

2022년 8월 9일

Great walkthrough w good explanations of the concepts used.

필터링 기준:

Graduate Admission Prediction with Pyspark ML의 7개 리뷰 중 1~7

교육 기관: Cheikh B

2021년 5월 13일

교육 기관: Feng J

2021년 5월 16일

교육 기관: Alexandra A

2021년 8월 26일

교육 기관: Charlene J

2022년 8월 10일

교육 기관: Carlos A P

2020년 10월 25일

교육 기관: Muhammad M

2020년 12월 25일

교육 기관: Aruparna M

2021년 1월 31일