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Course
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Build Recommendation Systems with PySpark
Learn to build scalable recommendation systems with PySpark. This course will teach you to predict user purchases using collaborative filtering, content-based methods, and real-time streaming, helping you deliver personalized shopping experiences.
What you'll learn
Online retailers and e-commerce platforms rely on recommendation systems to enhance customer experiences and drive sales. In this course, Build Recommendation Systems with PySpark, you’ll gain the ability to build scalable, data-driven recommendation models to predict user purchases.
First, you’ll explore the fundamentals of recommendation systems, including collaborative filtering and content-based methods.
Next, you’ll discover how to train and optimize recommendation models using PySpark MLlib, tuning hyperparameters and handling real-world challenges like cold starts.
Finally, you’ll learn how to deploy recommendation models in batch mode and real-time streaming using Spark Structured Streaming and Kafka.
When you’re finished with this course, you’ll have the skills and knowledge of recommendation systems with PySpark needed to deliver personalized shopping experiences at scale.
Table of contents
About the author
Bismark is a BI & Big Data Engineer obsessed with applying his knowledge in computer engineering and mathematics in the fields of Data Science, Artificial Intelligence, Machine Learning, Big Data, and Human Computer Interaction to find disease cures, provision of better healthcare and technology, autonomous systems, education and productivity through research into novel methods and algorithms for computation.
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