Building Recommender Systems with Machine Learning and AI
Updated with Tensorflow Recommenders (TFRS) and Generative Adversarial Networks for recommendations (GANs)
Learn how to build machine learning 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
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.
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What people are saying
recommender system
This a practical course for what is involved in building a recommender system.
The course is amazing ,the speaker and the team behind this course worked hard to bring out a collection of good content.But as told by many other reviews the focus is not so much on the coding but the essence of recommender system,And thats what I needed for now.Overall a great experience got to learn a lot of knew stuff.
I'll admit I'm not far into this course, but I bought it as a bundle with the other recommender system course and that one is MUCH better and deeper into actually learning and synthesizing what's going on -- ie, what you need to be successful.
This course is very useful for practitioners in recommender system field.
In this course, I sort of viewed how those concepts are applied in the industry specifically in the recommender system area.
One caveat is that don't assume only watching this course will make you a master of building a killer recommender system, it's a roadmap to the rich RecSys world, we still need lots of time to practice ourselves.
But what I was looking for was to design a recommender system extensively on implicit ratings - like user behaviour on e-commerce platform.
I was waiting for a recommender system course.
I am so excited to be able to implement my own recommender system.
My goal after this course is to able to propose a recommender system to e-commerce in Burkina Faso.
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recommender systems
I love the way, Recommender Systems are explained in this training.
Real experiences from top companies 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).
Was clear, would like more math though Yes, its good match, eager to understand recommender systems - How to Design / Develop a quick-slice and project-based course I like the approach of teaching covering from basics instead of searching everything this is awesome I am excepting real-time use cases as part of this course let me see how it goes.
Generally gives understandigs of recommender systems.
Seems to cover all the things I would like to learn about recommender systems and how to scale on AWS.
The course covers almost all areas of Recommender Systems to date, even tackles some experimental ideas through its "Bleeding Edge" videos.
I would have liked to see knowledge/profile based and rule based recommender systems being discussed.
That said the code provided is very good but be ready to spend some extra time trying to figure it out on your own Great course for someone with basic knowledge of machine learning, but needing an understanding of how that applies to recommender systems.
not yet Frank's in depth analysis and knowledge will give you a complete overview in the subject of recommender systems.
So really looking forward to learn recommender systems so that I can apply it practically I just realized that recommendation system is not as simple as i imagine.
I'm doing a data science course and we've just started on the topic of recommender systems so this is a great addendum Nice course Frank Kane is amazing teacher This course was well explained.
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far so good
The course is structured well and it's not hard to follow Very complete course So far so good.
Fantastic instructor So far so good I was surprised with the pragmatic approach in this course.
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step by step
It gives you only a list of algorithm and doesn't guide you step by step as other courses do.
I'm not really learning how to build a Recommendation System step by step.
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well explained
The coding can get a bit heavy to understand in some points, but overall the main take home concepts were very well explained and concise.
There is also a lot of Python magic used as well as matrix operations that are used not well explained.
very interesting course on recommendation systems, but the examples do not seem well explained As all courses taught by Frank this one as well is filled with great insights.
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real world
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.
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.
This course is excellent in that it is always focused on the real world application.
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frank kane
Frank Kane is really Good.
I have taken many classes from Instructor Frank Kane.
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too fast
Feels like the subject has gotten too complicated too fast.
too fast and not really detail when explained It's good to begin understanding many of the concepts of Machine Learning but the coding parts are almost all given and the interactivity is low.
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many things
Covered many things in deep but not focused on coding part much.
The course is quite good as far as the intent and spectrum is concerned, but it seems t be packing too many things in a single course.
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almost all
So far this guy sounds very professional, his voice and speed are perfect and over all i am enjoying this very much The course is an overall cover for almost all types of recommender system.
recommendation systems
I have some experience creating POCs for recommendation systems and this course might just be the right one to scale my skills up He is running like anything.
Lots of books talks about Recommendation Systems but this course has the content to begin with implementation.
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Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
Research Scientist-Machine Learning $55k
Cloud Architect - Azure / Machine Learning $75k
Watson Machine Learning Engineer $81k
Machine Learning Software Developer $103k
Software Engineer (Machine Learning) $116k
Applied Scientist, Machine Learning $130k
Autonomy and Machine Learning Solutions Architect $131k
Applied Scientist - Machine Learning -... $136k
RESEARCH SCIENTIST (MACHINE LEARNING) $147k
Machine Learning Engineer 2 $161k
Machine Learning Scientist Manager $170k
Machine Learning Scientist, Personalization $213k
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