Dan Foreman Mackey has an extensive array of github resources at https://github.com/dfm including Markov Chain Monte Carlo toolkit, Fast Gaussian Processes for regression, Scalable 1D Gaussian Processes, etc.
Trinket: online python simulator, can be useful for devices and platforms that don't easily support python: https://trinket.io/
Gaussian Process Regression package written in C++ and Python: Developed and maintained by Sivaram Ambikasaran, Dan-Foreman Mackey et al.; Fast Direct Methods for Gaussian Processes, Ambikasaran et al. 2014, http://adsabs.harvard.edu/abs/2014arXiv1403.6015A
The classic book "Artificial Intelligence: A Modern Approach" by Russell & Norvig has homeworks in multiple languages and great examples: http://aima.cs.berkeley.edu/
Gaussian Process Regression: Free and comprehensive book describing the theory of Gaussian Processes and applications to science and machine learning. Very often cited as the basis for many astrophysics and exoplanet applications of GP regression. Rasmussen & Williams 2006
TVMA (Toolkit for Multivariate Data Analysis) is a CERN-based machine learning environment that is a good example of tools developed specifically for scientific topics: https://root.cern.ch/tmva