Anomaly Detection with Isolation Forest and Kernel Density Estimation

Anomaly detection is to find data points that deviate from the norm. In other words, those are the points that do not follow expected patterns. Outliers and exceptions are terms used to describe unusual data. Anomaly detection is important in a variety of fields because it gives valuable and actionable insights. An abnormality in an MR imaging scan, for instance, might indicate tumorous region in the brain, while an anomalous readout from a manufacturing plant sensor could indicate a broken […]

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How to Learn a Machine Learning Algorithm

Last Updated on August 15, 2020 The question of how to learn a machine learning algorithm has come up a few times on the email list. In this post I’ll share with you the strategy I have been using for years to learn and build up a structured description of an algorithm in a step-by-step manner that I can add to, refine and refer back to again and again. I even used it to write a book. This was just […]

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How to Implement a Machine Learning Algorithm

Last Updated on August 12, 2019 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. Kick-start your project with my new book Master Machine Learning Algorithms, including step-by-step tutorials and the Excel Spreadsheet files for all examples. Let’s get started. Photo by Maura McDonnell, some rights […]

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The Best Machine Learning Algorithm

Last Updated on August 12, 2019 What is the best machine learning algorithm? I get this question a lot. Maybe even daily. Sometimes it’s a general question. I figure people want to make sure they are learning the one true machine learning algorithm and not wasting their time on anything less. Most other times it is with regard to a specific problem. I think it’s a very good question, a very telling question. It tells me straight away that a […]

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Benefits of Implementing Machine Learning Algorithms From Scratch

Last Updated on August 15, 2020 Machine Learning can be difficult to understand when getting started. There are a lot of algorithms and processes that are prescribed and used, many with difficult to penetrate explanations for how and why the work. It can feel overwhelming. An approach that you can use to get handle on machine learning algorithms and practices is to implement them from scratch. This will give you a deep understanding of how the algorithm works and all […]

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How to Build an Intuition for Machine Learning Algorithms

Last Updated on December 13, 2019 Machine learning algorithms are complex. To get good at applying a given algorithm you need to study it from multiple perspectives: algorithmic, mathematical and empirical. It’s this last point I want to stress. You need to build up an intuition or how an algorithm behaves on real data. You need to work on lots of problems. In this post I want to encourage you to use small in-memory datasets when starting out and when […]

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How to Study Machine Learning Algorithms

Last Updated on August 12, 2019 Algorithms make up a big part of machine learning. You select and apply machine learning algorithms to build a model from your data, select features, combine the predictions from multiple models and even evaluate the capabilities of a given model. In this post you will review 5 different approaches that you can use to study machine learning algorithms. Kick-start your project with my new book Master Machine Learning Algorithms, including step-by-step tutorials and the […]

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How to Research a Machine Learning Algorithm

Last Updated on August 12, 2019 Algorithms are a big part of the field of machine learning. You need to understand what algorithms are out there, and how to use them effectively. An easy way to shortcut this knowledge is to review what is already known about an algorithm, to research it. In this post you will discover the importance of researching machine learning algorithms and the 5 different sources that you can use to accelerate your understanding of machine […]

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How To Investigate Machine Learning Algorithm Behavior

Last Updated on December 13, 2019 Machine learning algorithms are complex systems that require study to understand. Static descriptions of machine learning algorithms are a good starting point, but are insufficient to get a feeling for how the algorithm behaves. You need to see the algorithm in action. Experimenting on a running machine learning algorithms will allow you to build an intuition for the cause and effect relationship of the algorithm parameters with the results you can achieve on different […]

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Don’t Start with Open-Source Code When Implementing Machine Learning Algorithms

Last Updated on August 12, 2019 Edward Raff is the author of the Java Machine Learning library called JSAT (which is an acronym for Java Statistical Analysis Tool). Edward has implemented many algorithms in creating this library and I recently reached out to him and asked what advice he could give to beginners implementing machine learning algorithms from scratch. In this post we take a look at tips on implementing machine learning algorithms based on Edwards advice. Kick-start your project with […]

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