Sundog Education by Frank Kane
Updated with 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.
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.
Instructor Details
Courses : 7
Specification: Building Recommender Systems with Machine Learning and AI
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28 reviews for Building Recommender Systems with Machine Learning and AI
Add a review
| Price | $17.99 |
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| Provider | |
| Duration | 11.5 hours |
| Year | 2021 |
| Level | Intermediate |
| Language | English |
| Certificate | Yes |
| Quizzes | No |
$17.99
Haoxuan Xu –
The instructor is very clear in his explanation. The course is structured well and it’s not hard to follow
Santosh –
I love the way, Recommender Systems are explained in this training. It has touched upon every possible area and given a glimpse of future research too. And most importantly, the experience of the instructor(Mr. Kane) in the same field has made this training even more informative. He has nice teaching skills. This training really assisted me to pick a thesis research topic and grow professionally. Regards.
Jeffrey –
up to this point, this is very good
Kunle Ibitoye –
Learnt so much already in the first few minutes. Explains very clearly. Frank Kane is really Good.
Esther de Oliveira –
Frank explains really well all the concepts, always giving examples to make it easier to understand and his previous experience helps so he can give important insights about the subject.
Kartikay Khosla –
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.
Riddhi Rathod –
It is amazing
Robert Levy –
This a practical course for what is involved in building a recommender system. The course covers a lot of algorithms, explains the theory and provides a lot of code to get going with. Doing the examples requires some coding and a bit of time to write, debug and run (especially if you’re using a regular PC). Frank Cane focuses most on what works in real life, and distinguishes between interesting techniques that are unproven and those that have been battle hardened.
Haridas K Pillai –
Fantastic
Varun kadekar –
Good match. I have some experience creating POCs for recommendation systems and this course might just be the right one to scale my skills up
Alejandro Fontanella –
Interesting what I learnt up to this lesson.
Rohit Pathak –
Great Course , like how the instructor is trying to get us interested in the subject. Looking forward to more of his courses.
Mattia Pennacchietti –
Great Lecturer explaining clearly each topic.
Raja sekhar –
Since , I have prior knowledge I was able to follow it.
Leandro Correa Goncalves –
Very good content! It covers the main aspects around Recommender Systems with a good framework in Python that enables to quickly test the algorithms and evaluate the outcomes and also an introduction to Deep Learning which is very useful to understand some concepts that is currently being applied on Recommender Systems. The instructor is very nice and also has very good teaching skills!
Loubna Mekouar –
Very good. Thanks, Frank.
Abhinav Arya –
Star trek
Nagarjuna Varikoti –
I get a feel that this instructor is not a hands on programmer, but just reading the script. I think, a programmer with equally good articulation would make this course more appealing.
Melony Dias –
The presenter is very articulate (and has a great voice for on line lectures)
Omphile Seiphemo Louw –
Best intuitive explanations of mathematical concepts.
I Ketut Resika Arthana –
The voice of instructor too fast. Instructor not clear explain the algorithm/coding, better with ilustration or diagram.
Jasan –
good high level discussions of Rec systems but you will not be ready to build even a basic recommender on your own post this course. The exercises consist mostly of just hitting play on downloaded code and not building any recommender from scratch. But if you have any real interest you can use the downloaded code to understand on your own.
Bjarne Gerdes –
Pretty good explanations. Thanks for the content.
Jorge Faieta –
Excellent explanations and exercises, totally recommended
Tuvshintur Tserendorj –
Not much code being explained
Kai Cao –
many specific topic help understand scenario
JP Deka –
I enrolled in Frank’s other course titled ‘Machine Learning, Data Science and Deep Learning with Python’ few months ago and will be completing it within this week. I kept the recommender systems lectures in that course towards the end since my friends in Amazon told me that those are very important topics if you want a job tomorrow in Amazon and I finished those lectures yesterday. And now, I am here ready to begin my lectures in this course and I am sure I will love it a lot.
Jo o Alves Henriques –
Compared to previous courses I took in Udemy this is clearly the worst. The instructor can’t catch my attention, is always explaining formulas and algorithms that I can understand for myself and find on the internet. No one will understand the code for what he is saying, that is like 5 to 10% of the mind work you will have to do to understand it so its useless. The only positive part in this course is the theoretical part. Is it worth the money for the lazyness of reading on the Internet? That’s for you to decide, I am not asking for a refund because I consumed it, but this course is terrible.