Do you want some tips and tricks that are useful in developing successful machine learning applications? This is the subject of a journal article from 2012 titled “A Few Useful Things to Know about Machine Learning” (PDF) by University of Washing professor Pedro Domingos. It’s an in interesting read with a great opening hook: developing successful machine […]
If you don’t know what algorithm to use on your problem, try a few. Alternatively, you could just try Random Forest and maybe a Gaussian SVM. In a recent study these two algorithms were demonstrated to be the most effective when raced against nearly 200 other algorithms averaged over more than 100 data sets. In […]
Machine Learning has been commoditized into a service. This is a recent trend that looks like it will develop into the mainstream like commoditized storage and virtualization. It is the natural next step. In this review you will learn about BigML that provides commoditized machine learning as a service for business analysts and application integration. […]
Weka is the perfect platform for studying machine learning. It provides a graphical user interface for exploring and experimenting with machine learning algorithms on datasets, without you having to worry about the mathematics or the programming. In a previous post we looked at how to design and run an experiment running 3 algorithms on a […]
Need Help Getting Started with Applied Machine Learning? These are the Step-by-Step Guides that You’ve Been Looking For! What do you want help with? The most common question I’m asked is: “how do I get started?” My best advice for getting started in machine learning is broken down into a 5-step process: Step 1: Adjust Mindset. […]
Having one or two algorithms that perform reasonably well on a problem is a good start, but sometimes you may be incentivised to get the best result you can given the time and resources you have available. In this post, you will review methods you can use to squeeze out extra performance and improve the […]
