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Feature Engineering with PySpark

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Your job is to find the meaning in chaos. Careful curation is required to create toys datasets like MTCars and Iris. The data must be transformed in order to make them useful for machine-learning algorithms that can predict, extract, classify, cluster, etc. This course will cover the details that data scientists spend between 70 and 80% of their time dealing, such as feature engineering and data wrangling. Let's use PySpark Big Data to reduce these datasets which are becoming increasingly complex.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Supervised Learning with scikit-learn

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Introduction to PySpark

What You Will Learn

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This course will cover the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering

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In this chapter learn to remove unneeded information, handle missing values and add additional data to your analysis

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In this chapter learn how to create new features for your machine learning model to learn from

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In this chapter we'll learn how to choose which type of model we want

Course Instructors

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John Hogue

Lead Data Scientist, General Mills

I have a strong drive for innovation and giving back. Through my work I enjoy building out a career path and center of excellence for those in data science at General Mills. I have a passion for taki...

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Course Cover

$12

$6

49% OFF

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