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2020-datafest-maskman

ab-testing-cndgdr

Medium Article https://medium.com/@ozdogar/frequentist-vs-bayesian-a-b-testing-1f2e94e33515

ai-projects

Artificial Intelligence projects, documentation and code.

aind

Artificial Intelligence Projects

bandit_algorithms_conormm

Bandit Algorithm. https://medium.com/@conrmcdonald/solving-multiarmed-bandits-a-comparison-of-epsilon-greedy-and-thompson-sampling-d97167ca9a50

bayesian

Bayesian A/B testing techniques

causal-inference-tutorial

Repository with code and slides for a tutorial on causal inference.

causalml

Uplift modeling and causal inference with machine learning algorithms

cleanandgetdataproject

Coursera Clean and Get Data Course Project

codesmith-talk

common_datasets

coursera_machinelearning

Coursera Machine Learning Class

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

datasci_project_initiative

How to start a successful data science initiative, project and team.

datasciencecoursera

datasciencecoursera

datasharing

The Leek group guide to data sharing

dcla2019

Data Con LA 2019: Refining Customer Segments with Location presentation

deep-learning-v2-pytorch

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

django-star-social

Simple Social Site

django-web1

Django Python Website Example

dlnd

Deep Learning Foundation Projects

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

dplyr-tutorial

ds_models

A collection of common data science models and visualizations.

econml

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.

estmpgshinyapp

exdata_plotting1

Plotting Assignment 1 for Exploratory Data Analysis

exdata_plotting2

fast-style-transfer

TensorFlow CNN for fast style transfer ⚡🖥🎨🖼

findclosestball