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Learn

Explore a library of educational materials (workshops, guides, books, videos, etc.) covering a wide range of ML-related topics, tools, and workflows. From foundational concepts to advanced techniques, these materials offer clear explanations, practical examples, and actionable insights to help you navigate the complexities of ML with confidence.

GeoDeepDive: Unlocking Knowledge from Scientific Literature

Scientific Text Mining

A guide to using GeoDeepDive, a powerful platform and database for large-scale text and data mining of scientific documents.

Computational and Inferential Thinking: The Foundations of Data Science

The Computational and Inferential Thinking (CIT) book is provided here as a resource for those who need a more gentle introduction to AI. This book begins by covering basic…

Peter Cruz Parrilla

Interpretable Machine Learning

Interpretability

Interpretable Machine Learning (IML) provides a concise and substantially deep breakdown of many classic machine learning models that serve as the foundation for most…

Peter Cruz Parrilla

MLflow 101: Local Experiment Tracking for Beginners

Reproducibility

This blog is a beginner-friendly walkthrough of local experiment tracking with MLflow. If you’ve ever lost a “good” result because you forgot which settings you used, MLflow…

Exploring Fact-Based QA with RAG: Romeo and Juliet

Prompt engineering

I demo’d this notebook at ML+Coffee on May 7, 2025. It’s an early work in progress, but I hope it’s still useful in its current state! I plan to integrate this tutorial into…

Learning Through Comparison: Use Cases of Contrastive Learning

Contrastive learning

Multimodal learning

Representation learning

Contrastive learning is reshaping how models learn, driving widespread progress in feature learning, clustering, out-of-distribution detection, and multimodal applications.…

Yin Li, Chris Endemann

Introduction to Statistical Learning

Statistical learning

Unsupervised Learning

An Introduction to Statistical Learning (ISL) is a foundational book for anyone interested in statistical learning and its applications in data analysis. Authored by Gareth…

One Useful Thing

Industry applications

One Useful Thing is a blog where Wharton professor Ethan Mollick “provides a research-based view on the implications of AI.” Since November 2022 Mollick has been sharing a…

Trustworthy AI - Explainability, Bias, Fairness, and Safety

Anomaly detection

Computer vision

The Trustworthy AI: Explainability, Bias, Fairness, and Safety workshop equips participants with practical skills to evaluate and improve the trustworthiness of machine…

Intro to AWS SageMaker for Predictive ML/AI

This introductory AWS SageMaker workshop teaches core workflows for running predictive ML/AI models in AWS SageMaker, an AWS-managed machine learning environment.…

Exploring the Titanic Dataset

The Titanic dataset is a well-known dataset that contains information about the passengers of the Titanic ship.It includes variables such as age, gender, class, fare, and…

Grokking

Empirical patterns

The verb, “to grok”, was originally coined by Robert A. Heinlein in his 1961 science fiction novel “Stranger in a Strange Land,” where it meant to understand something so…

Intro to Python

The Plotting and Programming in Python workshop provides an introduction to programming in Python 3 for people with little or no previous programming experience. It uses…

Intro to Machine Learning with Sklearn

The Intro to Machine Learning with Sklearn workshop will walk you through introductory machine learning concepts as well as how to implement common ML methods (e.g.…

Intro to Deep Learning with Keras

The Intro to Deep Learning with Keras workshop will walk you through introductory deep learning concepts as well as how to build a neural networks in Keras. Keras is…

Intro to Deep Learning with PyTorch

The Intro to Deep Learning with PyTorch workshop from Udacity will walk you through introductory deep learning concepts as well as how to build a neural networks in PyTorch.…

Understanding Deep Learning

Nowadays, nearly anyone can implement a deep learning model in a just a few lines of code. What separates the novices from the experts, however, is the ability to understand…

Intro to Text Analysis / NLP

The Intro to Text Analysis workshop introduces the field of Natural Language Processing (NLP) and how to gain insights from collections of text data (i.e., a corpus). This…

Overview of Reproducibility Lecture

Reproducibility

Dr. Sarah Stevens’ lecture highlights the critical importance of reproducibility in computational and data science projects. She also shares best practices to ensure…

Version Control with GitHub Desktop

Reproducibility

Navigating the world of version control systems like Git can initially feel daunting, especially for those new to programming or collaborative software development projects.…

Version Control with Git and GitHub (Carpentries)

Reproducibility

The below video (and the 7 subsequent videos in the workshop playlist) will walk you through this introductory Git workshop from the Carpentries: Version Control with…

Intro to Out-of-Distribution Detection

The below tutorial from Sharon Li, an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison, introduces a pervasive problem faced…

How to Contribute?

We want Nexus to serve also as a place where members of the community can share their knowledge. This guide answers the question, how to contribute to Nexus?