Building Recommender Systems with Machine Learning and AI — 93% OFF Coupon

How to create machine learning recommendation systems with deep learning, collaborative filtering, and Python.

★4.4 out of 5
50,532 students
English
Updated May 2026

Quick Facts — Building Recommender Systems with Machine Learning and AI Overview

Here's a quick overview of everything you need to know about Building Recommender Systems with Machine Learning and AI before you enroll:

•Course Name: Building Recommender Systems with Machine Learning and AI
•Platform: Udemy
•Instructor: Frank Kane, Sundog Education by Frank Kane, Sundog Education Team
•Coupon Last Verified: May 31, 2026
•Level: All Levels
•Topic: Development
•Subtopic: Recommendation Engine
•Total Time: 12h of video content
•Language: English
•Access Type: Unlimited lifetime access + updates
•Certificate: Included upon completion from Udemy
•Main Skills: 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)
•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)
•Current Price: $11.99 (was $179.99). You save $168.00 with 93% discount.
•How to Apply: Click the coupon button to activate your discount automatically
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Skills You'll Master in This Course

By the end of Building Recommender Systems with Machine Learning and AI, you'll have these practical skills:

✓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.

Prerequisites for This Course

Before enrolling in Building Recommender Systems with Machine Learning and AI, make sure you have:

•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.

About This Udemy Course

The following is the full official course description for Building Recommender Systems with Machine Learning and AI as published on Udemy by instructor Frank Kane, Sundog Education by Frank Kane, Sundog Education Team:

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.

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.

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
  • Building modern recommenders with neural collaborative filtering
  • 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. 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.

High-quality, hand-edited English closed captions are included to help you follow along.

I hope to see you in the course soon!

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Is the Building Recommender Systems with Machine Learning and AI Coupon Worth It?

Expert review by Andrew Derek, Lead Course Analyst at CoursesWyn.•Last updated: May 31, 2026.

Based on analysis of the curriculum structure, student engagement metrics, and verified rating data, Building Recommender Systems with Machine Learning and AI is a high-value resource for learners seeking to build skills inDevelopment. Taught by Frank Kane, Sundog Education by Frank Kane, Sundog Education Team on Udemy, the 12h course provides a structured progression from foundational concepts to advanced techniques— making it suitable for learners at all levels. The current coupon reduces the price by 93%, from $179.99 to $11.99, removing the primary financial barrier to enrollment.

✓What We Like (Pros)

  • Verified 93% price reduction makes this course accessible to learners on any budget.
  • Aggregate student rating of 4.4 out of 5 indicates high learner satisfaction.
  • Strong enrollment base with over 50,532 students demonstrates course popularity and trust.
  • Includes an official Udemy completion certificate and lifetime access to all future content updates.

!Keep in Mind (Cons)

The following limitations should be considered before enrolling in Building Recommender Systems with Machine Learning and AI:

  • The depth of Development coverage may be challenging for absolute beginners without the listed prerequisites.
  • Lifetime access is contingent on the continued operation of the Udemy platform.
  • Hands-on projects and quizzes require additional time investment beyond video watch time.
Final Verdict: Worth It
This course offers exceptional value with current pricing
✓

Course Rating Summary

Building Recommender Systems with Machine Learning and AI has earned an aggregate rating of 4.4 out of 5 from 50,532 verified student reviews on Udemy. Below is the detailed rating distribution showing learner satisfaction across all star levels.

4.4
★★★★★
50,532 Verified Ratings
5 stars
75%
4 stars
15%
3 stars
6%
2 stars
2%
1 star
2%

* Rating distribution is approximated from the aggregate score. Sourced from Udemy.

About the Instructor — Frank Kane, Sundog Education by Frank Kane, Sundog Education Team

Building Recommender Systems with Machine Learning and AI is taught by Frank Kane, Sundog Education by Frank Kane, Sundog Education Team, a Udemy instructor specializing in Development. For the full instructor biography, professional credentials, and a complete list of their courses, visit the official instructor profile on Udemy.

•Instructor Name: Frank Kane, Sundog Education by Frank Kane, Sundog Education Team
•Subject Area: Development
•Teaching Approach: Practical, project-based instruction focused on real-world application of Development skills.

Frequently Asked Questions

The following questions and answers cover the most common queries about Building Recommender Systems with Machine Learning and AI, its coupon code, pricing, and enrollment process.

About the Author

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Andrew Derek

Lead Course Analyst at CoursesWyn with 8+ years of experience evaluating online learning platforms. I've analyzed 500+ Udemy courses and helped thousands of learners choose the right courses for their career goals.

⭐4.8/5 Rating
✓Trusted by 10K+ Students

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