Building Recommender Systems with Machine Learning and AI
Building Recommender Systems with Machine Learning and AI, How to create machine learning recommendation systems with deep learning, collaborative filtering, and Python.
Course Description
Updated with Neural Collaborative Filtering (NCF), Tensorflow Recommenders (TFRS) and Generative Adversarial Networks for recommendations (GANs). Learn how to build machine learning recommendation systems from one of Amazon’s pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon’s personalized product recommendation systems.
This Building Recommender Systems with Machine Learning and AI course takes you all the way from the early days of collaborative filtering, to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the best items to every individual user.
The coding exercises in this Recommender Systems with Machine Learning and AI course use the Python programming language. We include an intro to Python if you’re new to it, but you’ll need some prior programming experience in order to use this course successfully. Learning how to code is not the focus of this course; it’s the algorithms we’re primarily trying to teach, along with practical examples. We also include a short introduction to deep learning if you are new to the field of artificial intelligence, but you’ll need to be able to understand new computer algorithms.
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 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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Who this course is for:
- Software developers interested in applying machine learning and deep learning to product or content recommendations
- Engineers working at, or interested in working at large e-commerce or web companies
- Computer Scientists interested in the latest recommender system theory and research
