Machine Learning with PySpark: Recommender System

Did you know that personalized product recommendations can increase sales by up to 20%? As consumers, we all appreciate suggestions tailored to our tastes, and as AI engineers, we can harness data to deliver that experience. This Guided Project was created to help data analysts and AI enthusiasts learn how to build scalable recommendation systems to enhance customer experience and drive sales. This 2-hour project-based course will teach you how to construct a data processing pipeline using PySpark, implement K-means clustering with OpenAI text embeddings, and develop a recommendation system that suggests products based on user behavior.

To achieve this, you will create a personalized product recommendation system by working through a real-world scenario where an e-commerce company needs to improve its recommendation capabilities. This project is unique because it combines powerful tools like PySpark and OpenAI’s embeddings for hands-on experience in creating data-driven recommendations. To be successful in this project, you should have basic Python programming skills, familiarity with data processing libraries like Pandas, a basic understanding of machine learning concepts, and some experience with APIs and data manipulation using SQL or PySpark.