Here I write about GMMs because it's a method of clustering that serves as a basis for more advanced concepts.

The explanation for Gaussian Processes from CS229 Notes is the best I found and understood http://cs229.stanford.edu/section/cs229-gau...

Kernels provide a bridge between linearity and non-linearity. If an algorithm can be expressed only in terms of a inner product between tw...

Here I cover the bayesian approach for linear regression and how bayes is used to implement predictors.

En estas notas se realizan pruebas con la estructura de datos "Series".

En estas notas hago pruebas con la estructura de datos DataFrame.

This is the final part of what I've learned from I Heart Stats: Learning to Love Statistics about Inferential Statistics.

Learning some ML approaches based on probability theory, here are my notes on learning about Maximum A Posteriori (MAP) .