A game theoretic approach to explain the output of any machine learning model.

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

Data and code behind the articles and graphics at FiveThirtyEight

Free MLOps course from DataTalks.Club

An open-source, low-code machine learning library in Python

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b

Collection of useful data science topics along with articles, videos, and code

Open-source demos hosted on Dash Gallery

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Code, Notebooks and Examples from Practical Business Python

PyMC educational resources

Source code accompanying O'Reilly book: Machine Learning Design Patterns

A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.

Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry thr…

Machine Learning University: Accelerated Tabular Data Class

Time series forecasting with machine learning models

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.

Cursos completos de IA dictados por Humai

Python-centered read-along of Forecasting: Principles and Practice

Amazon SageMaker workshops: Introduction, TensorFlow in SageMaker, and more

Interactive visualization dashboard in Python with Panel

distfit is a python library for probability density fitting.

Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

Python port of CausalImpact R library

K-Means clustering - constrained with minimum and maximum cluster size. Documentation: https://joshlk.github.io/k-means-constrained

🧪 Simple data science experimentation & tracking with jupyter, papermill, and mlflow.