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Building Recommender Systems with Machine Learning and AI

40,403 Enrolled Students

Course Features

Partner
Level
intermediate
Start Date
Duration
1 year
Access
Full lifetime access
Certificate
Certificate of completion

Building Recommender Systems with Machine Learning and AI

40,403 Enrolled Students

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

Requirements

  • 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.

Description

Updated with 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.

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
  • Using the latest frameworks from Tensorflow (TFRS) and Amazon Personalize.
  • 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
  • "Bleeding edge alerts" covering the latest research in the field of recommender systems

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!

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

Instructors

About the instructor

Sundog Education by Frank Kane

Sundog Education’s mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford.

Sundog Education is led by Frank Kane and owned by Frank’s company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computingdata mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

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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 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

Requirements

  • 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.

Description

Updated with 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.

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
  • Using the latest frameworks from Tensorflow (TFRS) and Amazon Personalize.
  • 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
  • “Bleeding edge alerts” covering the latest research in the field of recommender systems

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!

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