Materials for Georgetown Data Science certificate. http://scs.georgetown.edu/courses/1069/data-sources

Survival analysis built on top of scikit-learn

View flow data as Sankey diagrams

📃 Data Carpentry Python lessons on ecology.

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…

Slate is a Jekyll theme for GitHub Pages

Parallel computing in Python tutorial materials

Code that accompanies my talk at TF Dev Summit 2016

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Lime: Explaining the predictions of any machine learning classifier

Pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.

Methods to display time series, spatial and space-time data using R

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

Feather: fast, interoperable binary data frame storage for Python, R, and more powered by Apache Arrow

N-D labeled arrays and datasets in Python

Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Pytho…

The Jupyter Notebooks behind my OReilly report, "A Whirlwind Tour of Python"