Author and Publisher at MLtechniques.com. Machine learning scientist, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent also founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target).
Here is a simple trick that can solve a lot of problems. You can not trust a linear or logistic regression performed on data if… Read More »Simple Trick to Remove Serial Correlation in Regression Models
This simple introduction to matrix theory offers a refreshing perspective on the subject. Using a basic concept that leads to a simple formula for the… Read More »Gentle Approach to Linear Algebra, with Machine Learning Applications
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python,… Read More »29 Statistical Concepts Explained in Simple English – Part 13
This is another spectacular property of the exponential distribution, and also the first time an explicit formula is obtained for the variance of the range,… Read More »A Beautiful Result in Probability Theory
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python,… Read More »29 Statistical Concepts Explained in Simple English – Part 12
By Amita Kapoor and Ajit Jaokar. In this book, we introduce coding with tensorflow 2.0. We show how to develop with tensorflow 1.0 and contrast… Read More »Free Book: Getting Started with TensorFlow 2.0
The following resources and books were hand-picked, and curated by one of our interns. They cover many topics of interest to data scientists, and most… Read More »17 Great ML Books and Funny Pictures
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python,… Read More »33 Statistical Concepts Explained in Simple English – Part 10
Summary: Finally there are tools that let us transcend ‘correlation is not causation’ and identify true causal factors and their relative strengths in our models. This is… Read More »Causality – The Next Most Important Thing in AI/ML
So many fascinating and deep results have been written about the number (1 + SQRT(5)) / 2 and its related sequence – the Fibonacci numbers… Read More »Some Fun with Gentle Chaos, the Golden Ratio, and Stochastic Number Theory
