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120-data-science-interview-questions

Answers to 120 commonly asked data science interview questions.

airflow

Apache Airflow

arviz

Exploratory analysis of Bayesian models with Python

awesome-machine-learning-interpretability

A curated list of awesome machine learning interpretability resources.

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

bgnbd_python

Predictive CLV analytics and BGNBD implementation with Python

brms

brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

cheatsheets

RStudio Cheat Sheets

clean-code-ml

:bathtub: Clean Code concepts adapted for machine learning and data science

cntk

Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

cohort_analysis

Data wrangling and visualization (heatmap) for cohort analysis

cookiecutter-data-science

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

cs231n.github.io

Public facing notes page

customer-analytics

Customer Analytics & Financial Valuation for Zakka Canada. This is the associated iPython Notebook code.

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

df2016_workshops

doing_bayesian_data_analysis

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

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.

eng-practices

Google's Engineering Practices documentation

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

fastai

The fastai deep learning library, plus lessons and tutorials

fasttext

Library for fast text representation and classification.

ggplot_exercise_using_diamond_data

ggpubr

'ggplot2' Based Publication Ready Plots

gitignore

A collection of useful .gitignore templates

grf

Generalized Random Forests

h2o-3

Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

iml

iml: interpretable machine learning R package

kedro

A Python library for building robust production-ready data and analytics pipelines

keras

Deep Learning for humans