pourya-ir's Stars
khangich/machine-learning-interview
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
matplotlib/cheatsheets
Official Matplotlib cheat sheets
py-why/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.
TDAmeritrade/stumpy
STUMPY is a powerful and scalable Python library for modern time series analysis
rguo12/awesome-causality-algorithms
An index of algorithms for learning causality with data
databricks/Spark-The-Definitive-Guide
Spark: The Definitive Guide's Code Repository
pgmpy/pgmpy
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
dmlc/gluon-nlp
NLP made easy
CamDavidsonPilon/lifelines
Survival analysis in Python
liyin2015/python-coding-interview
A middle-to-high level open source algorithm book designed with coding interview at heart!
stanfordmlgroup/ngboost
Natural Gradient Boosting for Probabilistic Prediction
ycjuan/libffm
A Library for Field-aware Factorization Machines
mlrun/mlrun
MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
palash1992/GEM
trent-b/iterative-stratification
scikit-learn cross validators for iterative stratification of multilabel data
ibrahimjelliti/Deeplearning.ai-Natural-Language-Processing-Specialization
This repository contains my full work and notes on Coursera's NLP Specialization (Natural Language Processing) taught by the instructor Younes Bensouda Mourri and Łukasz Kaiser offered by deeplearning.ai
WillianFuks/tfcausalimpact
Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.
jmschrei/apricot
apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html
ryancheunggit/Denoise-Transformer-AutoEncoder
loft-br/xgboost-survival-embeddings
Improving XGBoost survival analysis with embeddings and debiased estimators
jeffheaton/jh-kaggle-util
Jeff Heaton's Kaggle Utilities
vivek-bombatkar/Databricks-Apache-Spark-2X-Certified-Developer
Databricks - Apache Spark™ - 2X Certified Developer
epsilon-machine/missingpy
Missing Data Imputation for Python
MaxHalford/sorobn
🧮 Bayesian networks in Python
amueller/dabl
Data Analysis Baseline Library
pylon-lib/pylon
florist-notes/CS228_PGM
🌀 Stanford CS 228 - Probabilistic Graphical Models
mlrun/demos
End to end MLRun demos
atif-hassan/FRUFS
An unsupervised feature selection technique using supervised algorithms such as XGBoost
ryancheunggit/tabular_dae