It's a few years old, but still acts as a great technical introduction to the topic.

It's a few years old, but still acts as a great technical introduction to the topic.

The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.

Customers are adopting Apache Spark ‒ an open-source distributed processing framework ‒ on Amazon EMR for large-scale machine learning workloads, especially for applications that power customer segmentation and content recommendation. By leveraging Spark ML, a set of machine learning algorithms included with Spark, customers can quickly build and execute massively parallel machine learning jobs.

The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.

Serious answer: I tend to dive deep into a particular algorithm...learning the math better, getting used to different applications of it, etc. So that's where I usually spend my time - along with the advice /u/Jigsus offered...focusing my learning around the kinds of needs I'm working on problem-/data-wise. Sounds like survival analysis, so I try to find as much material focused around that. On the flip side, I haven't done anything like sentiment analysis, so I know next to nothing about Naive Bayes text classification. I tend to read over a rather wide selection of ML and statistics blogs, so I'm not entirely unclear about such things, it's just that I don't spend a copious amount of time other than playing with a toy dataset now and then.