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carpentries-incubator / ml4bio-workshop

Materials for a workshop introducing machine learning to biologists
https://carpentries-incubator.github.io/ml4bio-workshop/
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Intermediate ML resources #94

Open agitter opened 4 years ago

agitter commented 4 years ago

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 commented 4 years ago

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

cmilica commented 4 years ago

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 commented 3 years ago

Machine learning resources list

agitter commented 3 years ago

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 commented 3 years ago

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

agitter commented 3 years ago

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

agitter commented 3 years ago

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

It builds on caret.

agitter commented 2 years ago

Navigating the pitfalls of applying machine learning in genomics

agitter commented 2 years ago

Carpentries introduction to deep learning.

agitter commented 1 year ago

REFORMS: Reporting Standards for Machine Learning Based Science