Learn advanced machine learning techniques using Excel. No coding required.
Wednesday, March 16, 2022
Tuesday, March 15, 2022
- : Fractal supervised clustering in GPU (graphics processing unit) using image filtering techniques akin to neural networks, automated black-box detection of the number of clusters, unsupervised clustering in GPU using density (gray levels) equalizer.
- : New test of independence, spatial processes, model fitting, dual confidence regions, minimum contrast estimation, oscillating estimators, mixture and surperimposed models, radial cluster processes, exponential-binomial distribution with infinitely many parameters, generalized logistic distribution.
- : Statistical distribution of distances and Rayleigh test, Weibull distribution, properties of nearest neighbor graphs, size distribution of connected components, geometric features, hexagonal lattices, coverage problems, simulations, model-free inference.
- : Random functions, random graphs, random permutations, chaotic convergence, perturbed Riemann Hypothesis (experimental number theory), attractor distributions in extreme value theory, central limit theorem for stochastic processes, numerical stability, optimum color palettes, cluster processes on the sphere.
- here.: 28 exercises with solution expanding the theory and methods presented in the textbook, well documented source code and formulas to generate various deviates and simulations, simple recipes (with source code) to design your own data animations as MP4 videos - see ours on YouTube,
About the Author
How to Obtain the Book?
In this article, I proposes a simple metric to measure predictive power. It is used for combinatorial feature selection, where a large number of feature combinations need to be ranked automatically and very fast, for instance in the context of transaction scoring, in order to optimize predictive models. This is about rather big data, and we would like to see an Hadoop methodology for the technology proposed here. It can easily be implemented in a Map Reduce framework. It was developed by the author in the context of credit card fraud detection, and click/keyword scoring. This material will be part of our data science apprenticeship, and included in our Wiley book.
A different way to do regression with prediction intervals. In Python and without math. No calculus, no matrix algebra, no statistical eng...
Despite my long statistical and machine learning career both in academia and in the industry, I never heard of complex random variables unti...
Learn advanced machine learning techniques using Excel. No coding required. It is amazing what you can do with a simple tool such as Excel. ...
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 ...