Building Recommender Systems with Machine Learning and AI, Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.
Created by Sundog Education by Frank Kane, Frank Kane
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What you'll 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 recommender systems 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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