Building Recommendation Engines 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

This course will show you how to build recommendation engines in PySpark with Alternating Least Squares. This course will show you how to create recommendation engines in PySpark using Alternating Least Squares. It uses both the MovieLens dataset and Million Songs. This course also contains the code required to train, test, and implement ALS models on various types of customer data.

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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Introduction to PySpark

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Supervised Learning with scikit-learn

What You Will Learn

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Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users

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You will also learn a very powerful way to uncover hidden features (latent features) that you may not even know exist in customer datasets

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You will also learn important techniques for properly preparing your data for ALS in Spark

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This will be the foundation for all subsequent ALS models you build using Pyspark

Course Instructors

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Jamen Long

Data Scientist

Jamen is a data scientist with experience building machine learning models to predict and guide customers physical and digital shopping journeys. Having started his data science journey with DataCamp...

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Course Cover

$12

$6

49% OFF

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