Nowadays, recommender systems are everywhere. for example, Amazon uses recommender systems to suggest some products that you might be interested in based on the products you've bought earlier. Or Spotify will suggest new tracks based on the songs you use to listen to every day. Most of these recommender systems use some algorithms which are based on Matrix factorization such as NMF( NON NEGATIVE MATRIX FACTORIZATION) or ALS (Alternating Least Square).
Music Recommender System Using Pyspark
Taught in English
Instructor: Ahmad Varasteh
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Recommended experience
(24 reviews)
What you'll learn
Learn how to setup the google colab for distributed data processing
Learn how aggregate a pyspark dataframe to have the data needed for our machine learning model
Learn how to use StringIndexer to convert a String (categorical) column into Unique Integral column
Learn how to create ALS model for Recommender System
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Guided Project
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Prepare the Google Colab for distributed data processing
Mounting our Google Drive into Google Colab environment
Importing csv file of our Dataset (4 Gb) into pySpark dataframe
Dropping some useless columns and nan Values in our dataframe
Performing an Aggregation to prepare the data
Learn how to use StringIndexer to convert a String (categorical) column into Unique Integral column
Creating ALS model for Recommender System
Recommended experience
Learners should be familiar with Python programming Language, Spark Technology and have a little experience working with google colab environment
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Reviewed on Mar 2, 2021
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