Building Recommender Systems with Machine Learning and AI
How to create machine learning recommendation systems with deep learning, collaborative filtering, and Python.
4.37 (3245 reviews)
48,040
students
12 hours
content
Jan 2025
last update
$94.99
regular price
What you will learn
Understand and apply user-based and item-based collaborative filtering to recommend items to users
Create recommendations using deep learning at massive scale
Build recommendation engines with neural networks and Restricted Boltzmann Machines (RBM's)
Make session-based recommendations with recurrent neural networks and Gated Recurrent Units (GRU)
Build a framework for testing and evaluating recommendation algorithms with Python
Apply the right measurements of a recommender system's success
Build recommender systems with matrix factorization methods such as SVD and SVD++
Apply real-world learnings from Netflix and YouTube to your own recommendation projects
Combine many recommendation algorithms together in hybrid and ensemble approaches
Use Apache Spark to compute recommendations at large scale on a cluster
Use K-Nearest-Neighbors to recommend items to users
Solve the "cold start" problem with content-based recommendations
Understand solutions to common issues with large-scale recommender systems
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1726410
udemy ID
6/1/2018
course created date
11/21/2019
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