A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

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 ;)

A collection of various deep learning architectures, models, and tips

Practice your pandas skills!

Recipes for using Python's pandas library

The "Python Machine Learning (3rd edition)" book code repository

Useful functions, tutorials, and other Python-related things

PyMC educational resources

Text and code for the second edition of Think Bayes, by Allen Downey.

Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)

Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques

An introduction to network analysis and applied graph theory using Python and NetworkX

Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke

Jupyter notebooks with exercises for the No bullshit guide to linear algebra.

Labs and Exercises from "An Introduction to Statistical Learning", implemented in Python.