Chevron Left
Volver a Building Machine Learning Pipelines in PySpark MLlib

Opiniones y comentarios de aprendices correspondientes a Building Machine Learning Pipelines in PySpark MLlib por parte de Coursera Project Network

4.3
estrellas
41 calificaciones
6 reseña

Acerca del Curso

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....

Principales reseñas

Filtrar por:

1 - 6 de 6 revisiones para Building Machine Learning Pipelines in PySpark MLlib

por Andrés M

7 de may. de 2021

I never write reviews, but ... please DON´T TAKE THIS PROJECT. Terrible project, no theoretical explanation, no explanation of functions, no complete project using pipelines (only 2 lines). The installation of libraries is not correct, I spent about two days trying to install them. Poor English language, zero explanations in general. It's a shame that people with high level of education are trying to scam people, I regret 100% of paying for this. I DID NOT LEARN ANYTHING.

por Aruparna M

21 de feb. de 2021

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

por 19BST035-HARI K R B B C

25 de sep. de 2020

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

por Cheikh B

27 de mar. de 2021

Awsome project and very good explaination thank you for this project

por Leonardo E

21 de nov. de 2020

pretty useful, actually.

por MD R I

5 de oct. de 2020

helpful project