Building Recommendation Engines with PySpark
|
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
This course will show you how to build recommendation engines using Alternating Least Squares in PySpark. Using the popular MovieLens dataset and the Million Songs dataset, this course will take you step by step through the intuition of the Alternating Least Squares algorithm as well as the code to train, test and implement ALS models on various types of customer data.
User Reviews
Be the first to review “Building Recommendation Engines with PySpark”
Related Products
Data Scientist Interview Preparation
Prepare for the data science interview process, from navigating job postings to passing the technical interview.
Machine Learning for Trading
Discover how to apply machine learning techniques to trading strategies and financial markets.
Build Deep Learning Models with TensorFlow
Use TensorFlow to build and tune deep learning models.
Build a Machine Learning Model
Learn to build machine learning models with Python.
Feature Engineering
Machine learning is only as good as its training data. Learn how to process data properly before training your models.
Learn R
Learn how to code and clean and manipulate data for analysis and visualization with the R programming language.
There are no reviews yet.