Kernel methods give a systematic and principled approach to training learning machines and the good generalization performance achieved can be readily justified using statistical learning theory or Bayesian arguments. We describe how to use kernel methods for classification, regression and novelty detection and in each case we find that training can be reduced to optimization of a convex cost function.

If you've ever asked yourself: "How do I choose the covariance function for a Gaussian process?" this is the page for you. Here you'll find concrete advice on how to choose a covariance function for your problem, or better yet, make your own.

This tutorial aims to provide an intuitive understanding of the Gaussian processes regression. Gaussian processes regression (GPR) models have been widely used in machine learning applications because of their representation flexibility and inherent uncertainty measures over predictions.

Has nice interactive examples and UMAP vs t-SNE

Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.

Includes lectures, lecture notes and assignments.

Lectures for Deep Learning: https://www.youtube.com/playlist?list=PLMsTLcO6etti_SObSLvk9ZNvoS_0yia57

Lectures for Reinforcement Learning: https://www.youtube.com/playlist?list=PLMsTLcO6ettgmyLVrcPvFLYi2Rs-R4JOE

A good set of best practices for deployment that isn't language-specific

Coding nowadays is a big part of ML and while it's important that the model works well, it's also important that the code is written properly too.

Link is the general python version, ML-specific version here: https://github.com/davified/clean-code-ml

Video version: https://bit.ly/2yGDyqT