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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
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). So in this Project, we are going to use ALS Algorithm to create a Music Recommender system to suggest new tracks to different users based upon the songs they've been listening to. As a very important prerequisite of this course, I suggest you study a little bit about ALS Algorithm because in this course we will not cover any theoretical concepts. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Programming Model
Algorithms
Algorithm Training
PySpark
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
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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