leodema / RL

Related projects: ⓘ

• bgweber / DS_Production
  ☆155Updated 3 years ago
• amueller / ml-workshop-2-of-4
  Intermediate Machine Learning with Scikit-learn, 4h interactive workshop
  ☆125Updated 3 years ago
• amueller / ml-workshop-4-of-4
  Advanced Machine Learning with Scikit-learn part II
  ☆162Updated 4 years ago
• Bergvca / pyspark_dist_explore
  Data Exploration in PySpark made easy - Pyspark_dist_explore provides methods to get fast insights in your Spark DataFrames.
  ☆100Updated 5 years ago
• tdpetrou / integrating-pandas-with-scikit-learn
  Tutorial covering a new workflow available going from pandas to scikit-learn
  ☆40Updated last year
• amueller / ml-workshop-3-of-4
  Advanced Machine Learning with Scikit-learn part I
  ☆139Updated 4 years ago
• amueller / dabl
  Data Analysis Baseline Library
  ☆130Updated 8 months ago
• tklouie / PyData_LA_2018
  Added repo for PyData LA 2018 tutorial
  ☆88Updated 5 years ago
• jem1031 / pandas-pipelines-custom-transformers
  Material for Talk at PyData Seattle 2017
  ☆168Updated 6 years ago
• numeristical / introspective
  Repo for the ML_Insights python package
  ☆145Updated 11 months ago
• chrispaulca / waterfall
  An easy to use waterfall chart function for Python
  ☆160Updated 3 years ago
• chrisalbon / sklearn-flask-docker
  ☆117Updated this week
• amueller / ml-workshop-1-of-4
  Introduction to Machine learning with Python, 4h interactive workshop
  ☆302Updated 4 years ago
• tgoldenberg / kedro-mlflow-example
  ☆42Updated last year
• EthicalML / explainability-and-bias
  ☆102Updated last year
• AustinRochford / PyCEbox
  ⬛ Python Individual Conditional Expectation Plot Toolbox
  ☆164Updated 4 years ago
• amesar / mlflow-spark-summit-2019
  MLFlow Spark Summit 2019 Presentation
  ☆67Updated 5 years ago
• mchrestkha / machine_learning_examples
  ☆11Updated 2 weeks ago
• TomAugspurger / dask-tutorial-pycon-2018
  ☆135Updated 4 years ago
• Affirm / shparkley
  Spark implementation of computing Shapley Values using monte-carlo approximation
  ☆74Updated last year
• EricSchles / drifter_ml
  A machine learning testing framework for sklearn and pandas. The goal is to help folks assess whether things have changed over time.
  ☆99Updated 2 years ago
• Automunge / AutoMunge
  Tabular feature encoding pipelines for machine learning with options for string parsing, missing data infill, and stochastic perturbation…
  ☆165Updated 3 weeks ago
• amueller / aml
  Applied Machine Learning with Python
  ☆76Updated 5 months ago
• klemag / PyconUS_2019-model-interpretability-tutorial
  Content for the Model Interpretability Tutorial at Pycon US 2019
  ☆42Updated last month
• knathanieltucker / deep-learning-building-blocks
  ☆32Updated last year
• tkrabel / edaviz
  edaviz - Python library for Exploratory Data Analysis and Visualization in Jupyter Notebook or Jupyter Lab
  ☆224Updated 4 years ago
• klemag / pydata_nyc2018-intro-to-model-interpretability
  Notebook and slides for my talk at Pydata NYC 2018
  ☆88Updated 3 months ago
• drivendataorg / nbautoexport
  Automatically export Jupyter notebooks to various file formats (.py, .html, and more) on save.
  ☆72Updated 7 months ago
• matthewbrems / missing-data-workshop
  Matt Brems' Missing Data Workshop
  ☆81Updated last year
• h2oai / driverlessai-recipes
  Recipes for Driverless AI
  ☆241Updated last week