Intermediate ML resources

[agitter]

Some participants may want to know where to turn next if they want to go deeper into machine learning. There may be too many of these to track. We can at least capture some that are particularly good.

Andreas Müller's Applied Machine Learning course: includes YouTube videos and slides

[agitter]

Amazon's Machine Learning University is now open to the public.

[cmilica]

For some reason, I can't find the right issue to add this to, so here it is here http://teachtogether.tech/en/index.html This is based on the Carpentries. Didn't really have a chance to look through it in detail, but could help with pedagogy.

[agitter]

Machine learning resources list

[agitter]

Machine learning in Python with scikit-learn is an online course from the scikit-learn team. Their modules follow some of our content but presumably focus on coding these things in scikit-learn.

[agitter]

Kaggle has Python-based Intro to Machine Learning lessons and exercises

[agitter]

How to avoid machine learning pitfalls: a guide for academic researchers

[agitter]

mikropml is an R package for supervised learning pipelines: paper, CRAN package

It builds on caret.

[agitter]

Navigating the pitfalls of applying machine learning in genomics

[agitter]

Carpentries introduction to deep learning.

[agitter]

REFORMS: Reporting Standards for Machine Learning Based Science