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 […]

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 […]

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 […]

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 […]