Stars
Curated list of data science interview questions and answers
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
This repository provides a command line interface (CLI) utility that replicates an Amazon Managed Workflows for Apache Airflow (MWAA) environment locally.
Lime: Explaining the predictions of any machine learning classifier
VIP cheatsheets for Stanford's CS 229 Machine Learning
Uplift modeling and causal inference with machine learning algorithms
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
Training deep learning models on AWS and GCP instances
ZenML 🙏: The bridge between ML and Ops. https://zenml.io.
Feature engineering package with sklearn like functionality
AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
Labs and demos for courses for GCP Training (http://cloud.google.com/training).
Examples of how to create colorful, annotated equations in Latex using Tikz.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Bayesian Modeling and Probabilistic Programming in Python
A library of sklearn compatible categorical variable encoders
An open source python library for automated feature engineering
Feature engineering package with sklearn like functionality
30 Days of React challenge is a step by step guide to learn React in 30 days. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
A book series on JavaScript. @YDKJS on twitter.