About this course: Machine learning is transforming the world around us. To become successful, you'd better know what kinds of problems can be solved with machine learning, and how they can be solved. Don't know where to start? The answer is one button away. During this course you will: - Identify practical problems which can be solved with machine learning - Build, tune and apply linear models with Spark MLLib - Understand methods of text processing - Fit decision trees and boost them with ensemble learning - Construct your own recommender system.

Nowadays, recommender systems are used to personalize your experience on the web, telling you what to buy, where to eat or even who you should be friends with. People's tastes vary, but generally follow patterns. People tend to like things that are similar to other things they like, and they tend to have similar taste as other people they are close with. Recommender systems try to capture these patterns to help predict what else you might like. E-commerce, social media, video and online news platforms have been actively deploying their own recommender systems to help their customers to choose products more efficiently, which serves win-win strategy.

It would be cool if there was a follow-up on how to do PCA approximate nearest neighbor search to do prediction online instead of just doing prediction in batch directly using SVD.