Jeremy Howard, formally of Kaggle gave a presentation at the University of San Francisco in mid 2013. In that presentation he touched on some of the broader benefits of machine learning competitions like those held on Kaggle. In this post you will discover 5 points I extracted from this talk that will motivate you to […]
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Colorado Reed from Metacademy wrote a great post recently titled “Level-Up Your Machine Learning” to answer the question he often receives of: What should I do if I want to get ‘better’ at machine learning, but I don’t know what I want to learn? In this post you will discover a summary of Colorado recommendations […]
In this post I lay out a concrete self-study roadmap for applied machine learning that you can use to orient yourself and figure out your next step. I think a lot about frameworks and systematic approaches (as evidenced on my blog). I would consider this post a vast expansion of my previous thoughts on a self-study […]
This is a guest post by Igor Shvartser, a clever young student I have been coaching. This post is part 3 in a 3 part series on modeling the famous Pima Indians Diabetes dataset that will investigate improvements to the classification accuracy and present final results (update: download from here). In Part 1 we defined the problem […]
The promise of Data Mining was that algorithms would crunch data and find interesting patterns that you could exploit in your business. The exemplar of this promise is market basket analysis (Wikipedia calls it affinity analysis). Given a pile of transactional records, discover interesting purchasing patterns that could be exploited in the store, such as offers […]
This is a project spotlight with Konstantin Slisenko a programmer and machine learning enthusiast. Could you please introduce yourself? My name is Konstantin Slisenko, I’m from Belarus. I graduated from the Belarusian State University of Informatics and Radioelectronics. I am currently taking a master course. I’m a Java developer and work in JazzTeam company. I like […]
Spot-checking algorithms is about getting a quick assessment of a bunch of different algorithms on your machine learning problem so that you know what algorithms to focus on and what to discard. In this post you will discover the 3 benefits of spot-checking algorithms, 5 tips for spot-checking on your next problem and the top […]