A part of having a good understanding of the machine learning problem that you’re working on, you need to know the data intimately. I personally find this step onerous sometimes and just want to get on with defining my test harness, but I know it always flushes out interested ideas and assumptions to test. As […]
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 […]
Machine learning is an iterative process rather than a linear process that requires each step to be revisited as more is learned about the problem under investigation. This iterative process can require using many different tools, programs and scripts for each process. A machine learning workbench is a platform or environment that supports and facilitates […]
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. […]
Implementing a machine learning algorithm in code can teach you a lot about the algorithm and how it works. In this post you will learn how to be effective at implementing machine learning algorithms and how to maximize your learning from these projects. Let’s get started. Benefits of Implementing Machine Learning Algorithms You can use […]
There are many paths into the field of machine learning and most start with theory. If you are a programmer then you already have the skills to decompose problems into their constituent parts and to prototype small projects in order to learn new technologies, libraries and methods. These are important skills for any professional programmer […]
Bojan Miletic asked a question about outlier detection in datasets when working with machine learning algorithms. This post is in answer to his question. If you have a question about machine learning, sign-up to the newsletter and reply to an email or use the contact form and ask, I will answer your question and may […]
Once you have defined your problem and prepared your data you need to apply machine learning algorithms to the data in order to solve your problem. You can spend a lot of time choosing, running and tuning algorithms. You want to make sure you are using your time effectively to get closer to your goal. […]
Programmers should get involved in the field of machine learning because they are uniquely skilled to make huge contributions. In this post you will learn that as a programmer it can be easy to overlook the skills you have and overvalue those things you don’t know. You will learn about four opportunities for programmers to […]
There are lots of things you can do to learn about machine learning. There are resources like books and courses you can follow, competitions you can enter and tools you can use. In this post I want to put some structure around these activities and suggest a loose ordering of what to tackle when in […]
