Alternatives and similar repositories for pydata2019-nlp-system:

Users that are interested in pydata2019-nlp-system are comparing it to the libraries listed below

• klemag / pydataLDN_2019-maintainable-code-for-data-science
  ☆47Updated 5 years ago
• cambridgespark / pydata-testing-for-data-science
  ☆25Updated 9 months ago
• amueller / ml-workshop-4-of-4
  Advanced Machine Learning with Scikit-learn part II
  ☆163Updated 4 years ago
• klemag / PyconUS_2019-model-interpretability-tutorial
  Content for the Model Interpretability Tutorial at Pycon US 2019
  ☆41Updated 7 months ago
• springcoil / probabilisticprogrammingprimer
  ☆90Updated 4 years ago
• amueller / ml-workshop-3-of-4
  Advanced Machine Learning with Scikit-learn part I
  ☆142Updated 4 years ago
• parsing-science / pydata_nyc_nov_2017
  ☆30Updated 7 years ago
• jem1031 / pandas-pipelines-custom-transformers
  Material for Talk at PyData Seattle 2017
  ☆168Updated 6 years ago
• ikding / pycon_time_series
  PyCon 2017 tutorial on time series analysis
  ☆72Updated 7 years ago
• WillKoehrsen / probabilistic-programming
  Experimenting with and teaching probabilistic programming
  ☆103Updated 2 years ago
• harnav / pydata-docker-tutorial
  Tutorial given at PyData LA 2018
  ☆97Updated 7 months ago
• numeristical / introspective
  Repo for the ML_Insights python package
  ☆150Updated last year
• bgweber / DS_Production
  ☆154Updated 4 years ago
• AlexIoannides / pymc-example-project
  Example PyMC3 project for performing Bayesian data analysis using a probabilistic programming approach to machine learning.
  ☆105Updated 6 years ago
• amueller / aml
  Applied Machine Learning with Python
  ☆78Updated last year
• ericmjl / bayesian-stats-talk
  Doing Bayesian statistics in Python!
  ☆67Updated 7 years ago
• philipmgoddard / pipelines
  demo of scikit-learn pipelines
  ☆27Updated last year
• knathanieltucker / deep-learning-building-blocks
  ☆33Updated 2 years ago
• klemag / pydata_nyc2018-intro-to-model-interpretability
  Notebook and slides for my talk at Pydata NYC 2018
  ☆88Updated 9 months ago
• ericmjl / data-testing-tutorial
  A short tutorial for data scientists on how to write tests for code + data.
  ☆119Updated 4 years ago
• docker-for-data-science / docker-for-data-science-tutorial
  Materials for "Docker for Data Science" tutorial presented at PyCon 2018 in Cleveland, OH
  ☆152Updated 4 years ago
• parsing-science / pymc3_quickstart_guide
  ☆80Updated 8 years ago
• fonnesbeck / intro_stat_modeling_2017
  Introduction to Statistical Modeling with Python (PyCon 2017)
  ☆166Updated 4 years ago
• tdpetrou / integrating-pandas-with-scikit-learn
  Tutorial covering a new workflow available going from pandas to scikit-learn
  ☆40Updated 2 years ago
• joeddav / get_smarties
  Dummy variable generation with fit/transform capabilities
  ☆23Updated 6 years ago
• dziganto / Data_Science_Fundamentals
  ☆102Updated 6 years ago
• croach / statistics-for-hackers
  A Jupyter notebook to accompany Jake VanderPlas's "Statistics for Hackers" talk from PyCon 2016.
  ☆77Updated 6 years ago
• darenasc / auto-eda
  Automated Exploratory Data Analysis. Simplifying Data Exploration
  ☆34Updated 4 years ago
• 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.
  ☆102Updated 3 years ago
• aswalin / Outlier-Impact-Treatment
  The goal of this repository is to detect the outliers for a dataset & see the impact of these outliers on predictive models
  ☆23Updated 6 years ago