TeamHG-Memex/eli5

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

Jupyter Notebooks for Springer book “Python for Probability, Statistics, and Machine Learning”

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.

Answers to the questions at the back of the chapters of Advances in Financial Machine Learning.

Example PyMC3 project for performing Bayesian data analysis using a probabilistic programming approach to machine learning.

Python tutorials for introduction to machine learning

This depot contain tutorials for real beginners who want to understand machine learning by reading some code.

COMS W4995 Applied Machine Learning - Spring 19