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 […]
Weka is the perfect platform for learning 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. A powerful feature of Weka is the Weka Experimenter interface. Unlike the Weka Explorer that is for filtering data […]
The test options you use when evaluating machine learning algorithms can mean the difference between over-learning, a mediocre result and a usable state-of-the-art result that you can confidently shout from the roof tops (you really do feel like doing that sometimes). In this post you will discover the standard test options you can use in […]
Weka makes learning applied machine learning easy, efficient, and fun. It is a GUI tool that allows you to load datasets, run algorithms and design and run experiments with results statistically robust enough to publish. I recommend Weka to beginners in machine learning because it lets them focus on learning the process of applied machine learning rather […]
The Systematic Process For Working Through Predictive Modeling Problems That Delivers Above Average Results Over time, working on applied machine learning problems you develop a pattern or process for quickly getting to good robust results. Once developed, you can use this process again and again on project after project. The more robust and developed your […]
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 […]
There are a lot of machine learning books and courses available and a trend towards free university courses and ebooks. With so much excellent resources available it can feel overwhelming. So much so that it may prevent you from getting started or making progress. In this post I want to share with you my tips […]
There is no right way to get into machine learning. We all learn slightly different ways and have different objectives of what we want to do with or for machine learning. A common goal is to get productive with machine learning quickly. If that is your goal then this post highlights five common mistakes programmers […]
There are a lot of good books on machine learning, but most people buy the wrong ones. A question I get asked the most is what books should people buy to get stared in machine learning. My answer to beginners is: “don’t buy textbooks“. In this post I want to point out a few key […]
