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Learner Reviews & Feedback for Building Machine Learning Pipelines in PySpark MLlib by Coursera Project Network

4.5
stars
31 ratings
4 reviews

About the Course

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 - 4 of 4 Reviews for Building Machine Learning Pipelines in PySpark MLlib

By 19BST035-HARI K R B B C

Sep 25, 2020

This Course is Very useful. This course big advantage is short. Read short, Learn Big.

By Leonardo E

Nov 21, 2020

pretty useful, actually.

By MD R I

Oct 5, 2020

helpful project

By Aruparna M

Feb 21, 2021

The dataset provided was wrong. It was not the exact one that was demonstrated by the instructor!