Recommender Systems and Deep Learning in Python | Bestseller Business Analytics & Intelligence course 80% off Course
Description
This course is a big bag of tricks that make recommender systems work across multiple platforms.
We’ll look at popular news feed algorithms, like Reddit, Hacker News, and Google PageRank.
We’ll look at Bayesian recommendation techniques that are being used by a large number of media companies today.
But this course isn’t just about news feeds.
Companies like Amazon, Netflix, and Spotify have been using recommendations to suggest products, movies, and music to customers for many years now.
These algorithms have led to billions of dollars in added revenue.
So I assure you, what you’re about to learn in this course is very real, very applicable, and will have a huge impact on your business.
For those of you who like to dig deep into the theory to understand how things really work, you know this is my specialty and there will be no shortage of that in this course. We’ll be covering state of the art algorithms like matrix factorization and deep learning (making use of both supervised and unsupervised learning - Autoencoders and Restricted Boltzmann Machines), and you’ll learn a bag full of tricks to improve upon baseline results.
As a bonus, we will also look how to perform matrix factorization using big data in Spark. We will create a cluster using Amazon EC2 instances with Amazon Web Services (AWS). Most other courses and tutorials look at the MovieLens 100k dataset - that is puny! Our examples make use of MovieLens 20 million.
Whether you sell products in your e-commerce store, or you simply write a blog - you can use these techniques to show the right recommendations to your users at the right time.
If you’re an employee at a company, you can use these techniques to impress your manager and get a raise!
I’ll see you in class!
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