scikit-learn

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Carefully curated resource links for data science in one place

A python library for decision tree visualization and model interpretation.

a delightful machine learning tool that allows you to train, test, and use models without writing code

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

Sequential model-based optimization with a `scipy.optimize` interface

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

An intuitive library to add plotting functionality to scikit-learn objects.

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

A modular active learning framework for Python

Feature engineering and selection open-source Python library compatible with sklearn.

Python library for portfolio optimization built on top of scikit-learn

Using python and scikit-learn to make stock predictions

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Genetic Programming in Python, with a scikit-learn inspired API

For extensive instructor led learning

Python AutoML for Trading Systems and Sports Betting

Automated Machine Learning on Kubernetes