A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Data and code behind the articles and graphics at FiveThirtyEight

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery 🧑‍🔬

Book about interpretable machine learning

ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

A list of Machine Learning Art Colabs

Enabling journalists, citizen scientists, humanitarian workers and others to detect “patterns of interest” in satellite imagery.

Guide to methods for generating and testing Cloud-Optimized Geospatial Formats

Resources for a 3.5 hour workshop on machine learning using the MLJ toolbox

A tutorial book of workflows for research using NASA EarthData in the Cloud created by the NASA-Openscapes team

Code for the paper "Neural hierarchical models of ecological populations"