In this installment of Understanding Bayes I want to discuss the nature of Bayesian evidence and conclusions. In a previous post I focused on Bayes factors’ mathematical structure and visualization. In this post I hope to give some idea of how Bayes factors should be interpreted in context. How do we use the Bayes factor to come to a conclusion?

We run our cluster analysis with great expectations, hoping to uncover diverse segments with contrasting likes and dislikes of the brands they use. Instead, too often, our K-means analysis returns the above graph of parallel lines indicating that the pattern of high and low ratings are the same for everyone but at different overall levels. The data come from the R package semPLS and look very much like what one sees with many customer satisfaction surveys.

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At Dataquest, we’ve released an interactive course on Spark, with a focus on PySpark. We explore the fundamentals of Map-Reduce and how to utilize PySpark to clean, transform, and munge data. In this post, we’ll dive into how to install PySpark locally on your own computer and how to integrate it into the IPython Notebbok workflow.

Hi there! This guide is for you:
• You’re new to Machine Learning.
• You know Python. (At least the basics! If you want to learn Python, try Dive Into Python.)
I learned Python by hacking first, and getting serious later. I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself.

Say you have a thousand columns and a million rows in your data set. Whichever way you look at it – small, medium or big data – you won’t be able to actually look at it. Zoom it in or out. Fit it into one screen. Blame human nature but most of us understand a subject better when they get to see a bigger picture. Is there a way to put your data in one image and navigate it almost like you would do with a map? Deep Learning combined with Topological Data Analysis can do exactly that and more.