Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
created at June 30, 2012, 6:39 p.m.
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
created at Jan. 14, 2013, 3:46 p.m.
Tools, wrappers, etc... for data science with a concentration on text processing
created at Nov. 3, 2013, 4:13 p.m.
Recipes for using Python's pandas library
created at Dec. 21, 2013, 5:14 p.m.
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
created at March 3, 2014, 4:08 p.m.
Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers
created at March 13, 2014, 8:23 a.m.
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
created at March 30, 2014, 5:34 a.m.
Jupyter notebooks from the scikit-learn video series
created at April 6, 2015, 2:08 a.m.
A Julia package for Gaussian Processes
created at April 30, 2015, 2:46 p.m.
Python package for Bayesian Machine Learning with scikit-learn API
created at July 30, 2015, 3:15 a.m.
Efficient Image Captioning code in Torch, runs on GPU
created at Nov. 20, 2015, 1:27 a.m.
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
created at Feb. 16, 2016, 7:48 p.m.
Notebooks and code for the book "Introduction to Machine Learning with Python"
created at May 29, 2016, 6:29 p.m.