Lists (23)
AI
Anomaly detection
Civic Tech
Data
Datasets
Embeddings
Feature engineering
Graphs
Hugo
Interviews process
LLM
MacOS
Med
ML
NLP
PhD
Products
Remote
Scrapers
Sustaintability
Synthetic Data
Time series
Visualization
Stars
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Examples and guides for using the OpenAI API
Python Data Science Handbook: full text in Jupyter Notebooks
A game theoretic approach to explain the output of any machine learning model.
A guidance language for controlling large language models.
12 Weeks, 24 Lessons, IoT for All!
Companion webpage to the book "Mathematics For Machine Learning"
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
Automatic extraction of relevant features from time series:
Notebooks and code for the book "Introduction to Machine Learning with Python"
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
The fastest way to create an HTML app
Create delightful software with Jupyter Notebooks
An easy to use blogging platform, with enhanced support for Jupyter Notebooks.
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
MIMIC Code Repository: Code shared by the research community for the MIMIC family of databases
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Repository for benchmarking graph neural networks (JMLR 2023)
Single-document unsupervised keyword extraction
Synthetic data generators for tabular and time-series data
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
The book every data scientist needs on their desk.
Natural Intelligence is still a pretty good idea.
General Assembly's Data Science course in Washington, DC
Tutorial for Sentiment Analysis using Doc2Vec in gensim (or "getting 87% accuracy in sentiment analysis in under 100 lines of code")
Library for clinical NLP with spaCy.