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

Produced in 2020

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 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

Quality Score

Content Quality
Video Quality
Qualified Instructor
Course Pace
Course Depth & Coverage

Overall Score : 88 / 100

Course Description

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!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

*Some courses are excluded from this sale. Coupon not working? If the link above doesn't drop prices, clear the cookies in your browser and then click this link here.
Also, you may need to apply the coupon code directly on the cart page to get the discount.

Coupon Code

Instructor Details

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 computing, data 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.
Due to our volume of students we are unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.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 computing, data mining

Students also recommend






99 total reviews

5 star 4 star 3 star 2 star 1 star
% Complete
% Complete
% Complete
% Complete
% Complete

the instructor has deep knowledge of the domain and the content explanation is excellent

The course covered the basics all the way to the more complex and leading-edge systems. Frank's previous experience at Amazon and IMDB allowed him to provide insights into the development of RS systems, their algorithms, and tricks used, without jeopardizing confidentiality. Highly "recommended" (pun intended without an algorithm).

It is a good course of introduction in Recommender Systems with DL, NN and ML. I think it will be an interesting student and people who want to to what it is.

Great course on recommendation system.Everything was new for me, at the end of the course learned a lot of great thing, whether it's algorithm or real world challenges in the system. Must do course, if really interested in the topic..

Very insightful, well organized, current, and practical. I greatly appreciated leaving with a folder full of Python samples and functions!

Initially I was a bit at sea with the Recommender framework introduced so rapidly ( that really should be extended; the framework is not a trivial thing for an average user and so should have been explained in greater depth and with smaller examples at first) but after slogging through the earlier parts the later portions became much easier. Also the last modules are where the course truly shines and that section should be updated with more cutting edge research and real world examples.All in all, a solid course and a good value for money but could be improved.

So far, everything is relevant and as an advanced Python user / data scientist I am happy to see that this focuses on Recommender Systems primarily, instead of covering everything in between (a common problem in Udemy courses).I've given 4 stars, because once I wanted to implement these methods on my own datasets, it's been quite rough to reproduce the code, since it's hidden under layers of classes. I would prefer a much simpler way of running these in order to reproduce it easier later.

Good course, not for beginners. Covered many things in deep but not focused on coding part much. I recommend going through python programming language before doing this course.

Very great course with working examples and discussions about a lot of techniques and algorithms. The numerous Heads-Up about choosing them are very valuable.The case studies and the experience of the teacher in the area are a good plus.As always Franks explanations are direct and pleasing to follow! Strongly recommended!!

I was looking forward to more informative information on the script involved, at least what the SVD Algorithm accomplished and how.

Great course, really surpassed my expectations already when I'm halfway through! one thing that could be better though would be highlighting the line of code you are talking about.

This course is very useful for practitioners in recommender system field. I would not start my tasks without this course. But you need to spend much time on studying source code because the information in the lecture is very limited.Frank is an expert on recommend system with rich industry experience. He give many good advises on real word recommend systems. It should be better if the course material can be designed more elaborately.