Chevron Left
Building Machine Learning Pipelines in PySpark MLlib(으)로 돌아가기

Coursera Project Network의 Building Machine Learning Pipelines in PySpark MLlib 학습자 리뷰 및 피드백

4.3
별점
54개의 평가

강좌 소개

By the end of this project, you will learn how to create machine learning pipelines using Python and Spark, free, open-source programs that you can download. You will learn how to load your dataset in Spark and learn how to perform basic cleaning techniques such as removing columns with high missing values and removing rows with missing values. You will then create a machine learning pipeline with a random forest regression model. You will use cross validation and parameter tuning to select the best model from the pipeline. Lastly, you will evaluate your model’s performance using various metrics. A pipeline in Spark combines multiple execution steps in the order of their execution. So rather than executing the steps individually, one can put them in a pipeline to streamline the machine learning process. You can save this pipeline, share it with your colleagues, and load it back again effortlessly. Note: You should have a Gmail account which you will use to sign into Google Colab. 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:

Building Machine Learning Pipelines in PySpark MLlib의 8개 리뷰 중 1~8

교육 기관: Andrés M

2021년 5월 7일

교육 기관: Jeremy S

2022년 1월 26일

교육 기관: Aruparna M

2021년 2월 21일

교육 기관: 19BST035-HARI K R B B C

2020년 9월 25일

교육 기관: Cheikh B

2021년 3월 27일

교육 기관: Leonardo E

2020년 11월 21일

교육 기관: MD R I

2020년 10월 5일

교육 기관: Sankirna J

2022년 5월 2일