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

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 proper measurements of a recommender system’s success
• Build recommender systems with matrix factorization methods like 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
A Windows, Mac, or Linux PC with a minimum of 3GB of free disc space .
Some experience with a programming or scripting language (preferably Python)
Some computing background, and a capability to know new algorithms.
New! Updated for Tensorflow 2, Amazon Personalize, and more.
Learn how to make recommender systems from one of Amazon’s pioneers within the sector . Frank Kane spent over nine years at Amazon, where he managed and led the event of the various 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 study your unique interests, and show the only products or content for you as a personal . These technologies became central to the most important , 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 supported neighborhood-based collaborative filtering, and work our high 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 during this course expecting a learn-to-code kind of format. There’s no recipe to follow on the thanks to make a recommender system; you’d wish to know the varied algorithms and therefore the thanks to choose when to use all for a given situation. We assume you already skills to code.

However, this course is extremely hands-on; you’ll develop your own framework for evaluating and mixing many different recommendation algorithms together, and you’ll even build your own neural networks using Tensorflow to urge 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 primary days of collaborative filtering, to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the only items to every individual user.

The coding exercises during this course use the Python programming language . We include an intro to Python if you’re new it, but you’ll need some prior programming experience so on use this course successfully. We also include a quick introduction to deep learning if you’re new the world of AI , 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 determine you within the course soon!

Who this course is for:
Software developers interested by applying machine learning and deep learning to product or content recommendations
Engineers working at , or interested by working at large e-commerce or web companies
Computer Scientists interested by the most recent recommender system theory and research
Featured review
Very good course, but the scholar will need to do plenty of off-the-clock research (read papers, single step through code watching variables not explained) to use the high level material within the course. there’s also plenty of Python magic used also as matrix operations that are used not well explained. None the less, the category could also be a bargain with unique industry experience that’s worth its weight in gold.

Course content
14 sections • 118 lectures • 10h 6m total length
Getting Started
Introduction to Python [Optional]
Evaluating Recommender Systems
A Recommender Engine Framework
Content-Based Filtering
Neighborhood-Based Collaborative Filtering
Matrix Factorization Methods
Introduction to Deep Learning [Optional]
Deep Learning for Recommender Systems
Scaling it Up
Real-World Challenges of Recommender Systems
Case Studies
Hybrid Approaches
Wrapping Up
Created by: Sundog Education by Frank Kane, Frank Kane
Last updated 8/2020
Direct Download Available
Rating: 4.6 out of 54.6
(1,585 ratings)
11,625 students

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