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學生對 Coursera Project Network 提供的 Building Machine Learning Pipelines in PySpark MLlib 的評價和反饋

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54 個評分

課程概述

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....

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1 - Building Machine Learning Pipelines in PySpark MLlib 的 8 個評論(共 8 個)

創建者 Andrés M

2021年5月7日

創建者 Jeremy S

2022年1月26日

創建者 Aruparna M

2021年2月21日

創建者 19BST035-HARI K R B B C

2020年9月25日

創建者 Cheikh B

2021年3月27日

創建者 Leonardo E

2020年11月21日

創建者 MD R I

2020年10月5日

創建者 Sankirna J

2022年5月2日