Alternatives and similar repositories for Data_Science_Fundamentals

Users that are interested in Data_Science_Fundamentals are comparing it to the libraries listed below

• ikding / pycon_time_series
  PyCon 2017 tutorial on time series analysis
  ☆72Updated 8 years ago
• amueller / ml-training-advanced
  Materials for the "Advanced Scikit-learn" class in the afternoon
  ☆166Updated 7 years ago
• bgweber / DS_Production
  ☆155Updated 5 years ago
• ritchieng / machine-learning-nanodegree
  Codes, notes and guides on Udacity's machine learning nanodegree.
  ☆82Updated 9 years ago
• WillKoehrsen / probabilistic-programming
  Experimenting with and teaching probabilistic programming
  ☆107Updated 3 years ago
• khanhnamle1994 / applied-machine-learning
  A step-by-step guide to get started with Applied Machine Learning
  ☆142Updated 7 years ago
• harnav / pydata-docker-tutorial
  Tutorial given at PyData LA 2018
  ☆97Updated last year
• rasbt / data-science-tutorial
  Code material for a data science tutorial
  ☆197Updated 8 years ago
• ankit-rathi / Data-Science-with-Python
  ☆33Updated last year
• amitkaps / full-stack-data-science
  Full Stack Data Science in Python
  ☆259Updated 7 years ago
• amueller / ml-workshop-4-of-4
  Advanced Machine Learning with Scikit-learn part II
  ☆164Updated 5 years ago
• woobe / h2o_tutorials
  Slides and code examples for H2O tutorials at various events
  ☆56Updated 8 years ago
• amueller / talks_odt
  Slides and materials for most of my talks by year
  ☆93Updated 2 years ago
• jphall663 / GWU_data_mining
  Materials for GWU DNSC 6279 and DNSC 6290.
  ☆240Updated 8 months ago
• croach / statistics-for-hackers
  A Jupyter notebook to accompany Jake VanderPlas's "Statistics for Hackers" talk from PyCon 2016.
  ☆77Updated 7 years ago
• jem1031 / pandas-pipelines-custom-transformers
  Material for Talk at PyData Seattle 2017
  ☆168Updated 7 years ago
• amueller / ml-workshop-3-of-4
  Advanced Machine Learning with Scikit-learn part I
  ☆143Updated 5 years ago
• Apress / mastering-ml-w-python-in-six-steps
  Source code for 'Mastering Machine Learning with Python in Six Steps' by Manohar Swamynathan
  ☆115Updated 8 years ago
• CristiVlad25 / ml-sklearn
  Tutorials on Machine Learning with Scikit-Learn
  ☆83Updated 5 years ago
• Startupsci / data-science-notebooks
  Jupyter notebooks for learning Python and Data Science, companion to Data Science Solutions book.
  ☆37Updated 5 years ago
• jayinai / ml-cheatsheet
  A constantly updated python machine learning cheatsheet
  ☆168Updated 8 years ago
• amitkaps / applied-machine-learning
  Applied Machine Learning @ http://amitkaps.com/ml
  ☆39Updated 6 years ago
• albahnsen / PracticalMachineLearningClass
  ☆90Updated 6 years ago
• amueller / scipy-2018-sklearn
  Scipy 2018 scikit-learn tutorial by Guillaume Lemaitre and Andreas Mueller
  ☆249Updated 7 years ago
• FavioVazquez / kaggle-learning
  Repository for sharing the knowledge from the learning path of Kaggle Learning. All contributions welcome :).
  ☆156Updated 8 years ago
• WillKoehrsen / data-science-for-good
  Data Science for Good Projects
  ☆50Updated 7 years ago
• robert-alvarez / pytorch_tutorial
  ☆31Updated 6 years ago
• etcrago / Tutorial-Arima-w-jeffrey-yau
  ☆98Updated 7 years ago
• Vishwacorp / nyc_uber_forecasting
  Forecasting Uber demand in NYC neighborhoods
  ☆34Updated 7 years ago
• MaartenGr / ML-API
  Guide on creating an API for serving your ML model
  ☆65Updated 3 years ago