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- 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
After a brief introduction to the course, we'll dive right in and install what you need: Anaconda (your Python development environment,) the course materials, and the MovieLens data set of 100,00 real movie ratings from real people. We'll then run a quick example to generate movie recommendations using the SVD algorithm, to make sure it all works!
There are many different flavors of recommender systems, and you encounter them every day. Let's review some of the applications of recommender systems in the real world.
Learn about different testing methodologies for evaluating recommender systems offline, including train/test, K-Fold Cross Validation, and Leave-One-Out cross-validation.
Measure how often your recommendations change (churn,) how quickly they respond to new data (responsiveness,) and why no metric matters more than the results of real, online A/B tests. We'll also talk about perceived quality, where you explicitly ask your users to rate your recommendations.
We'll walk through our sample code to apply our RecommenderMetrics module to a real SVD recommender using real MovieLens rating data, and measure its performance in many different ways.
We'll talk about how content-based recommendations work, and introduce the cosine similarity metric. Cosine scores will be used throughout the course, and understanding their mathematical basis is important.
Similarity between users or items is at the heart of all neighborhood-based approaches; we'll discuss how similarity measures fit into our architecture, and the effect data sparsity has on it.
We'll illustrate how user-based collaborative filtering works, where we recommend stuff that people similar to you liked.
Let's learn how PCA allows us to reduce higher-dimensional data into lower dimensions, which is the first step toward understanding SVD.
Let's run SVD and SVD++ on our MovieLens movie ratings data set, and evaluate the results. They're really good!
We'll cover a very simple neural network called the Restricted Boltzmann Machine, and show how it can be used to produce recommendations given sparse rating data.
Amazon open-sourced its recommender engine called DSSTNE, which makes it easy to apply deep neural networks to massive, sparse data sets and produce great recommendations at large scale.
Watch as I use SageMaker from a cloud-hosted Notebook to pre-process the MovieLens 1-million-rating data set, train and save a Factorization Machine model, and deploy the model for making real-time predictions for movie recommendations.
A huge number of commercial SAAS offerings have emerged to offer easy-to-use recommender systems out of the box, and there are many open-source offerings that allow you to develop recommender systems at scale at as low a level as you want. We'll cover some of the more popular ones, and enumerate the rest.
- A Windows, Mac, or Linux PC with at least 3GB of free disk space.
- Some experience with a programming or scripting language (preferably Python)
- Some computer science background, and an ability to understand new algorithms.
New! Updated for Tensorflow 2, Amazon Personalize, and more.
Learn how to build recommender 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 technologies.
You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them.
We'll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you'll learn from Frank's extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data.
Recommender systems are complex; don't enroll in this course expecting a learn-to-code type of format. There's no recipe to follow on how to make a recommender system; you need to understand the different algorithms and how to choose when to apply each one for a given situation. We assume you already know how to code.
However, this course is very hands-on; you'll develop your own framework for evaluating and combining many different recommendation algorithms together, and you'll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people. We'll cover:
Building a recommendation engine
Evaluating recommender systems
Content-based filtering using item attributes
Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF
Model-based methods including matrix factorization and SVD
Applying deep learning, AI, and artificial neural networks to recommendations
Session-based recommendations with recursive neural networks
Scaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machines
Real-world challenges and solutions with recommender systems
Case studies from YouTube and Netflix
Building hybrid, ensemble recommenders
This comprehensive 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 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. 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.
High-quality, hand-edited English closed captions are included to help you follow along.
I hope to see you in the course soon!
- 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