alielabridi

DGFraud-TF2

A Deep Graph-based Toolbox for Fraud Detection in TensorFlow 2.X

interpretability-tutorial-emnlp2020

Materials for the EMNLP 2020 Tutorial on "Interpreting Predictions of NLP Models"

radical-shapley-values

Python code to directly compute "radical" Shapley values for model features, by re-training the model on a subset of features on each iteration.

clickmodels

ClickModels is a small set of Python scripts for the user click models initially developed at Yandex. A Click Model is a probabilistic graphical model used to predict search engine click data from past observations. This project is aimed to deal with click models used in Information Retrieval (see next README.md) and intended to be easy-to-read and easy-to-modify. If it's not, please let me know how to improve it :)

allRank

allRank is a framework for training learning-to-rank neural models based on PyTorch.

tensor2tensor

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

wevi

Word Embedding Visual Inspector

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

tpot

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

snorkel

A system for quickly generating training data with weak supervision

facet

Human-explainable AI.

texthero

Text preprocessing, representation and visualization from zero to hero.

awesome-mlops

A curated list of references for MLOps

Awesome-Data-Science-Competition-Techniques

typedb-ml

TypeDB-ML is the Machine Learning integrations library for TypeDB

feature-engineering-for-machine-learning

Code repository for the online course Feature Engineering for Machine Learning

Amazing-Feature-Engineering

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

machine-learning-interview

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

abseil-cpp

Abseil Common Libraries (C++)

ydata-profiling

1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

imbalanced-learn

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

compose

A machine learning tool for automated prediction engineering. It allows you to easily structure prediction problems and generate labels for supervised learning.

hyperparameter-optimization

Implementation of Bayesian Hyperparameter Optimization of Machine Learning Algorithms

faker

Faker is a Python package that generates fake data for you.

pyod

A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

feature-selection-for-machine-learning

Code repository for the online course Feature Selection for Machine Learning

feature_engine

Feature engineering package with sklearn like functionality

graph_nets

PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

isolation-forest

A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.