Feature Engineering with PySpark

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Actualisé 01/2025

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

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Description du cours

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!

Conditions préalables

Introduction to PySpark Supervised Learning with scikit-learn

Exploratory Data Analysis

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Where to Begin

Description du cours

Earn Déclaration de réalisation

Inscrivez-vous .css-nklxlk{color:var(--wf-brand--main, #03EF62);}15 millions d’apprenants et commencer Feature Engineering with PySpark Aujourd’hui!

Créez votre compte gratuit

Inscrivez-vous 15 millions d’apprenants et commencer Feature Engineering with PySpark Aujourd’hui!