Building Recommendation Engines with PySpark

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

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

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Supervised Learning with scikit-learn

Introduction to PySpark

What You Will Learn

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

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

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

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

Course Content

Expand all sections

Module 1: Recommendations Are Everywhere

Module 2: How does ALS work?

Module 3: Recommending Movies

Module 4: What if you don't have customer ratings?

Course Instructors

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...