Recommender Systems and Deep Learning in Python

The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques What you'll learn: Understand and implement accurate recommendations for your users using simple and state-of-the-art algorithmsBig data matrix factorization on Spark with an AWS EC2 clusterMatrix factorization / SVD in pure NumpyMatrix factorization in KerasDeep neural networks, residual networks, and autoencoder in KerasRestricted Boltzmann Machine in Tensorflow Believe it or not, almost all online businesses today make use of recommender systems in some way or another.What do I mean by “recommender systems”, and why are they useful?Let’s look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook.Recommender systems form the very foundation of these technologies.Google:Search resultsThey are why Google is the most successful technology company today.YouTube: Video dashboardI’m sure I’m not the only one who’s accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that?That’s right. Recommender systems!Facebook: So powerful that world governments are worried that the newsfeed has too much influence on people! (Or maybe they are worried about losing their own power... hmm...)Amazing!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 busine