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30-Day Money-Back Guarantee

This course includes:

  • 10 hours on-demand video
  • 3 articles
  • Full lifetime access
  • Access on mobile and TV
Development Software Engineering Recommendation Engine

Building Recommender Systems with Machine Learning and AI

How to create recommendation systems with deep learning, collaborative filtering, and machine learning.
Bestseller
Rating: 4.6 out of 54.6 (1,577 ratings)
11,580 students
Created by Sundog Education by Frank Kane, Frank Kane
Last updated 8/2020
English
English
30-Day Money-Back Guarantee

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 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
Curated for the Udemy for Business collection

Course content

14 sections • 118 lectures • 10h 6m total length

  • Preview02:10
  • Preview09:05
  • Course Roadmap
    03:52
  • What Is a Recommender System?
    02:48
  • Preview03:22
  • Understanding You through Implicit and Explicit Ratings
    04:25
  • Top-N Recommender Architecture
    05:53
  • Preview04:46

  • [Activity] The Basics of Python
    05:04
  • Data Structures in Python
    05:17
  • Functions in Python
    02:46
  • [Exercise] Booleans, loops, and a hands-on challenge
    03:52

  • Preview03:49
  • Accuracy Metrics (RMSE, MAE)
    04:06
  • Top-N Hit Rate - Many Ways
    04:35
  • Coverage, Diversity, and Novelty
    04:55
  • Churn, Responsiveness, and A/B Tests
    05:06
  • [Quiz] Review ways to measure your recommender.
    02:55
  • [Activity] Walkthrough of RecommenderMetrics.py
    06:53
  • Preview05:08
  • [Activity] Measure the Performance of SVD Recommendations
    02:24

  • Our Recommender Engine Architecture
    07:27
  • [Activity] Recommender Engine Walkthrough, Part 1
    03:55
  • [Activity] Recommender Engine Walkthrough, Part 2
    03:51
  • [Activity] Review the Results of our Algorithm Evaluation.
    03:10

  • Preview08:58
  • K-Nearest-Neighbors and Content Recs
    03:59
  • [Activity] Producing and Evaluating Content-Based Movie Recommendations
    05:23
  • A Note on Using Implicit Ratings.
    03:36
  • [Activity] Bleeding Edge Alert! Mise en Scene Recommendations
    04:31
  • [Exercise] Dive Deeper into Content-Based Recommendations
    04:26

  • Preview04:49
  • Similarity Metrics
    08:32
  • Preview07:25
  • [Activity] User-based Collaborative Filtering, Hands-On
    04:59
  • Item-based Collaborative Filtering
    04:14
  • [Activity] Item-based Collaborative Filtering, Hands-On
    02:23
  • [Exercise] Tuning Collaborative Filtering Algorithms
    03:31
  • [Activity] Evaluating Collaborative Filtering Systems Offline
    01:28
  • [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering
    02:17
  • KNN Recommenders
    04:03
  • [Activity] Running User and Item-Based KNN on MovieLens
    02:25
  • [Exercise] Experiment with different KNN parameters.
    04:25
  • Bleeding Edge Alert! Translation-Based Recommendations
    02:29

  • Principal Component Analysis (PCA)
    Preview06:31
  • Singular Value Decomposition
    06:56
  • Preview03:46
  • Improving on SVD
    04:33
  • [Exercise] Tune the hyperparameters on SVD
    01:58
  • Bleeding Edge Alert! Sparse Linear Methods (SLIM)
    03:30

  • Important note about Tensorflow 2
    00:17
  • Important Tensorflow setup note!
    00:30
  • Deep Learning Introduction
    01:30
  • Deep Learning Pre-Requisites
    08:13
  • History of Artificial Neural Networks
    10:51
  • [Activity] Playing with Tensorflow
    12:02
  • Training Neural Networks
    05:47
  • Tuning Neural Networks
    03:52
  • Activation Functions: More Depth
    10:36
  • Introduction to Tensorflow
    11:29
  • [Activity] Handwriting Recognition with Tensorflow, part 1
    13:19
  • [Activity] Handwriting Recognition with Tensorflow, part 2
    12:03
  • Introduction to Keras
    02:48
  • [Activity] Handwriting Recognition with Keras
    09:52
  • Classifier Patterns with Keras
    03:58
  • [Exercise] Predict Political Parties of Politicians with Keras
    09:55
  • Intro to Convolutional Neural Networks (CNN's)
    08:59
  • CNN Architectures
    02:54
  • [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs)
    08:38
  • Intro to Recurrent Neural Networks (RNN's)
    07:38
  • Training Recurrent Neural Networks
    03:21
  • [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras
    11:01
  • Tuning Neural Networks
    04:39
  • Neural Network Regularization Techniques
    06:21

  • Intro to Deep Learning for Recommenders
    02:19
  • Restricted Boltzmann Machines (RBM's)
    Preview08:02
  • [Activity] Recommendations with RBM's, part 1
    12:46
  • [Activity] Recommendations with RBM's, part 2
    07:11
  • [Activity] Evaluating the RBM Recommender
    03:43
  • [Exercise] Tuning Restricted Boltzmann Machines
    01:43
  • Exercise Results: Tuning a RBM Recommender
    01:15
  • Auto-Encoders for Recommendations: Deep Learning for Recs
    04:27
  • [Activity] Recommendations with Deep Neural Networks
    07:23
  • Clickstream Recommendations with RNN's
    07:23
  • [Exercise] Get GRU4Rec Working on your Desktop
    02:42
  • Exercise Results: GRU4Rec in Action
    07:51
  • Bleeding Edge Alert! Deep Factorization Machines
    05:49
  • More Emerging Tech to Watch
    05:14

  • [Activity] Introduction and Installation of Apache Spark
    05:49
  • Apache Spark Architecture
    05:13
  • [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS
    06:02
  • [Activity] Recommendations from 20 million ratings with Spark
    04:57
  • Preview04:41
  • DSSTNE in Action
    09:25
  • Scaling Up DSSTNE
    02:14
  • AWS SageMaker and Factorization Machines
    04:24
  • SageMaker in Action: Factorization Machines on one million ratings, in the cloud
    07:38
  • Other Systems of Note (Amazon Personalize, RichRelevance, Recombee, and more)
    10:29
  • Recommender System Architecture
    10:14

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

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!

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

Featured review

Philip Lieberman
Philip Lieberman
57 courses
27 reviews
Rating: 5.0 out of 5a year ago
Very good course, but the student will need to do a lot of off-the-clock research (read papers, single step through code looking at variables not explained) to apply the high level material in the course. There is also a lot of Python magic used as well as matrix operations that are used not well explained. None the less, the class is a bargain with unique industry experience that is worth its weight in gold.

Instructors

Sundog Education by Frank Kane
Founder, Sundog Education. Machine Learning Pro
Sundog Education by Frank Kane
  • 4.5 Instructor Rating
  • 95,731 Reviews
  • 431,412 Students
  • 22 Courses

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 Kane
Founder, Sundog Education
Frank Kane
  • 4.5 Instructor Rating
  • 92,452 Reviews
  • 387,414 Students
  • 14 Courses

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, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.

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