An alternative to Generative Mixture Models. Access the full article here. I explain, with Python code and numerous illustrations, how to turn traditional tabular data into images, to perform both clustering and supervised classification using simple image filtering techniques. I also explain how to generalize the methodology to higher dimensions…

It is possible to design and deploy advanced machine learning algorithms that are essentially math-free and stats-free. People working on that are typically professional mathematicians. These algorithms are not necessarily simpler. See for instance a math-free regression technique with prediction intervals, here. Or supervised classification and alternative to t-SNE, here…

Finally published! This article discusses enriched visualizations, with a focus on animated gifs and videos built in Python. For instance, the comet video can feature several dimensions that are difficult to show in a static picture: the comet locations at any given time, the relative velocity of each comet, the…

This is not a traditional tutorial on linear algebra. The material presented here, in a compact style, is rarely taught in college classes. It covers a wide range of topics, while avoiding excessive use of jargon or advanced math. The fundamental tool is the power of a matrix, and its…

After 25 years of coding in C and Perl. As an independent author/researcher, there is of course nothing in my “job description” that says I should code in Python (or any other language). Yet for a long time, I thought coding in Python would help me a lot. It would…

A different way to do regression with prediction intervals. In Python and without math. No calculus, no matrix algebra, no statistical engineering, no regression coefficients, no bootstrap. Multivariate and highly non-linear. Interpretable and illustrated on synthetic data. Read more here. For years, I have developed machine learning techniques that barely…

This self-published book is dated July 2020 according to Amazon. But it appears to be an ongoing project. Like many new books, the material is on GitHub. The most recent version, dated June 2021, is available in PDF format. This is not a traditional book. It feels like a repository…

I share here my new article on synthetic data and interpretable machine learning. It will show you how to set up such an environment. I also mention three popular books published in the last three months. The figure below is from the first article featured in this newsletter. Article: synthetic…

Here I share my roadmap for the next 12 months. While I am also looking for external contributors and authors to add more variety, my focus — as far as my technical content is concerned — is to complete the following projects and publish the material on this platform. Summary …