We’re building Kaggle into a platform where you can collaboratively create all of your data science projects. This past quarter, we’ve increased the breadth and scope of work you can build on our platform by launching many new features and expanding computational resources.

Recently, I’ve been seeing a lot of services and products advertising automation of machine learning. Data Robot and H2O.ai offer platforms that allow the creation of machine learning algorithms in point-and-click interfaces. They’ll even do the feature engineering for you! This functionality, or something like it, is slowly being built into various tools and programs. They promise to automate the creation of the whole machine learning pipeline - from feature transformations, hyperparameter tuning, to model selection. There are open-source tools that do much the same things (like TPOT, a cool module I love the idea of but can never get to actually work on a data set that isn’t trivially small).

Most introductory tutorials on Gaussian processes start with a nose-punch of fancy statements, like:

Recently, while working on the Azure Data Lake R extension, I had to figure out a good way to create a zip file containing a package together with all its dependencies. This came down to understanding where does R store and search for packages. Despite the documentation, it did require additional reading and experimentation.

Why AlphaGo Zero Is Great

Hello everyone, welcome back! This post marks the beginning, hopefully, of your foray into the wonderful world of R and RStudio…and my delve into the odd vlog to go with the blog! I’ve brushed my hair for you, and I don’t do that for just anyone, so you’d better watch it at least once! So without further ado.

What is this post about ?

There are only a few well-known signals which I consider reliable. One of them is the Dow Theory. According to it, or at least to some interpretations of it, the bull market cycle almost ended this Friday.