#
Starred repositories
34
stars
written in Jupyter Notebook
Clear filter
A latent text-to-image diffusion model
Python Data Science Handbook: full text in Jupyter Notebooks
Learn how to design, develop, deploy and iterate on production-grade ML applications.
A game theoretic approach to explain the output of any machine learning model.
Python programs, usually short, of considerable difficulty, to perfect particular skills.
Companion webpage to the book "Mathematics For Machine Learning"
Practice your pandas skills!
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its go…
Language-Agnostic SEntence Representations
Debugging, monitoring and visualization for Python Machine Learning and Data Science
A python library for decision tree visualization and model interpretation.
A library for debugging/inspecting machine learning classifiers and explaining their predictions
🪼 a python library for doing approximate and phonetic matching of strings.
A small library for automatical adjustment of text position in matplotlib plots to minimize overlaps.
Source code for my collection of articles on using pandas.
A data visualization curriculum of interactive notebooks.
bamboolib - a GUI for pandas DataFrames
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Code to compute permutation and drop-column importances in Python scikit-learn models
Multiple Imputation with LightGBM in Python
Python implementation of the R stargazer multiple regression model creation tool
💊 Comparing causality methods in a fair and just way.
Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series
Data manipulation, analysis and visualisation in Python - specialist course Doctoral schools of Ghent University
There are always multiple ways to complete a task in Pandas. A minimal subset of the library is sufficient for almost everything.
stemgraphic python package for visualization of data and text