5 Mistakes Programmers Make when Starting in Machine Learning

Last Updated on June 18, 2016 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 make on the path to quickly being productive machine learning practitioners. Mistakes Programmers Make when Starting in Machine LearningPhoto […]

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How to get the most from Machine Learning Books and Courses

Last Updated on September 29, 2016 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 for self study that allow me to touch a resource once, extract everything I think I can learn from […]

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What is the Weka Machine Learning Workbench

Last Updated on August 16, 2020 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 a range of machine learning activities reducing or removing the need for multiple tools. Some statistical and machine learning […]

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Why you should be Spot-Checking Algorithms on your Machine Learning Problems

Last Updated on August 16, 2020 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. Photo by withassociates, some rights reserved 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 10 most popular data mining algorithms that you could use in your suite […]

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Applied Machine Learning Process

Last Updated on July 5, 2019 The Systematic Process For Working Through Predictive Modeling ProblemsThat 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 process, the faster you can get to reliable results. In this post, I want to share with you the skeleton […]

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How to Run Your First Classifier in Weka

Last Updated on August 22, 2019 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 than getting bogged down by the mathematics and the programming — those can come later. In this post, I want […]

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How To Choose The Right Test Options When Evaluating Machine Learning Algorithms

Last Updated on June 21, 2016 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 your algorithm evaluation test harness and how to choose the right options next time. Randomness The root of the […]

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Design and Run your First Experiment in Weka

Last Updated on August 22, 2019 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 and trying out different algorithms, the Experimenter is for designing and running experiments. The experimental results it produces are […]

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Quick and Dirty Data Analysis for your Machine Learning Problem

Last Updated on August 22, 2019 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 such, I use a step-by-step process to capture a minimum number of observations about the actual dataset before moving […]

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How to Tune a Machine Learning Algorithm in Weka

Last Updated on August 22, 2019 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. In a previous post we looked at how to design and run an experiment with 3 algorithms on a dataset and how to analyse and report the results. Manhattan Skyline, because we are going to be looking at […]

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