Feature Engineering with PySpark

Avançado

Actualizado 05/2025

Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.

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Descrição do curso

The real world is messy and your job is to make sense of it. Toy datasets like MTCars and Iris are the result of careful curation and cleaning, even so the data needs to be transformed for it to be useful for powerful machine learning algorithms to extract meaning, forecast, classify or cluster. This course will cover the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering. With size of datasets now becoming ever larger, let's use PySpark to cut this Big Data problem down to size!

Pré-requisitos

Supervised Learning with scikit-learn Introduction to PySpark

Exploratory Data Analysis

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Where to Begin

Descrição do curso

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Junte-se a mais .css-nklxlk{color:var(--wf-brand--main, #03EF62);}16 milhões de alunos e comece Feature Engineering with PySpark hoje!

Crie sua conta gratuita

Junte-se a mais 16 milhões de alunos e comece Feature Engineering with PySpark hoje!