Alternatives and similar repositories for COMS4995-s19

Users that are interested in COMS4995-s19 are comparing it to the libraries listed below

• dgkim5360 / the-elements-of-statistical-learning-notebooks
  Jupyter notebooks for summarizing and reproducing the textbook "The Elements of Statistical Learning" 2/E by Hastie, Tibshirani, and Frie…
  ☆292Updated 7 years ago
• amueller / scipy-2018-sklearn
  Scipy 2018 scikit-learn tutorial by Guillaume Lemaitre and Andreas Mueller
  ☆248Updated 6 years ago
• amueller / ml-workshop-1-of-4
  Introduction to Machine learning with Python, 4h interactive workshop
  ☆311Updated 5 years ago
• amueller / COMS4995-s20
  COMS W4995 Applied Machine Learning - Spring 20
  ☆247Updated 3 years ago
• amueller / ml-workshop-3-of-4
  Advanced Machine Learning with Scikit-learn part I
  ☆143Updated 5 years ago
• jphall663 / GWU_data_mining
  Materials for GWU DNSC 6279 and DNSC 6290.
  ☆239Updated 3 months ago
• rasbt / stat479-machine-learning-fs18
  Course material for STAT 479: Machine Learning (FS 2018) at University Wisconsin-Madison
  ☆492Updated 6 years ago
• jcrouser / islr-python
  ☆110Updated 4 years ago
• amueller / COMS4995-s18
  COMS W4995 Applied Machine Learning - Spring 18
  ☆157Updated 6 years ago
• LeiG / Applied-Predictive-Modeling-with-Python
  A collection of notebook to learn the Applied Predictive Modeling using Python.
  ☆279Updated 8 years ago
• PacktPublishing / Feature-Engineering-Made-Easy
  Feature Engineering Made Easy, published by Packt
  ☆216Updated 2 years ago
• gmaclenn / ml-flashcards-python
  Python script to pull machine learning flashcards from Chris Albon's twitter feed
  ☆81Updated 6 years ago
• rasbt / stat479-deep-learning-ss19
  Course material for STAT 479: Deep Learning (SS 2019) at University Wisconsin-Madison
  ☆541Updated 6 years ago
• amueller / ml-workshop-4-of-4
  Advanced Machine Learning with Scikit-learn part II
  ☆164Updated 5 years ago
• WillKoehrsen / automated-feature-engineering
  Automated feature engineering in Python with Featuretools
  ☆521Updated 6 years ago
• cmawer / pycon-2017-eda-tutorial
  Resources for the PyCon 2017 tutorial, "Exploratory data analysis in python"
  ☆237Updated 3 years ago
• neerjad / DataVisualization
  Tutorials on visualizing data using python packages like bokeh, plotly, seaborn and igraph
  ☆269Updated 5 years ago
• hse-aml / competitive-data-science
  Materials for "How to Win a Data Science Competition: Learn from Top Kagglers" course
  ☆660Updated 5 years ago
• amueller / ml-workshop-2-of-4
  Intermediate Machine Learning with Scikit-learn, 4h interactive workshop
  ☆128Updated 4 years ago
• bgweber / DS_Production
  ☆155Updated 4 years ago
• mervynlee94 / Advance-Machine-Learning
  ☆85Updated 7 years ago
• rasbt / stat479-machine-learning-fs19
  Course material for STAT 479: Machine Learning (FS 2019) taught by Sebastian Raschka at University Wisconsin-Madison
  ☆755Updated 4 years ago
• PacktPublishing / Python-Deep-Learning-Cookbook
  Python Deep Learning Cookbook, published by Packt
  ☆141Updated 2 years ago
• groverpr / Machine-Learning
  Notes for machine learning
  ☆219Updated 2 years ago
• jayinai / kaggle-classification
  A compiled list of kaggle competitions and their winning solutions for classification problems.
  ☆271Updated 9 years ago
• cs109 / a-2017
  Public Repository for cs109a, 2017 edition
  ☆327Updated 2 years ago
• jayinai / kaggle-regression
  A compiled list of kaggle competitions and their winning solutions for regression problems.
  ☆148Updated 9 years ago
• davidyakobovitch / data_science_resources
  📊 Data Science Resources, Data Science Standards & Machine Learning Pipelines
  ☆161Updated 3 years ago
• ParrotPrediction / docker-course-xgboost
  Materials for an online-course - "Practical XGBoost in Python"
  ☆219Updated 9 years ago
• klemag / pydata_nyc2018-intro-to-model-interpretability
  Notebook and slides for my talk at Pydata NYC 2018
  ☆88Updated last year