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Feature Engineering with PySpark

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

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4 Hours16 Videos60 Exercises
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Course Description

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!
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In the following Tracks

Big Data with PySpark

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  1. 1

    Exploratory Data Analysis

    Free

    Get to know a bit about your problem before you dive in! Then learn how to statistically and visually inspect your dataset!

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    Where to Begin
    50 xp
    Where to begin?
    50 xp
    Check Version
    100 xp
    Load in the data
    100 xp
    Defining A Problem
    50 xp
    What are we predicting?
    100 xp
    Verifying Data Load
    100 xp
    Verifying DataTypes
    100 xp
    Visually Inspecting Data / EDA
    50 xp
    Using Corr()
    100 xp
    Using Visualizations: distplot
    100 xp
    Using Visualizations: lmplot
    100 xp
  2. 3

    Feature Engineering

    In this chapter learn how to create new features for your machine learning model to learn from. We'll look at generating them by combining fields, extracting values from messy columns or encoding them for better results.

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GroupTraining 2 or more people?

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In the following Tracks

Big Data with PySpark

Go To Track

Datasets

2017 St Paul MN Real Estate Dataset

Collaborators

Collaborator's avatar
Adrián Soto
Collaborator's avatar
Nick Solomon
John Hogue HeadshotJohn Hogue

Lead Data Scientist, General Mills

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