A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

🍊 📊 💡 Orange: Interactive data analysis

Python code for common Machine Learning Algorithms

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

A python library for decision tree visualization and model interpretation.

Text Classification Algorithms: A Survey

For extensive instructor led learning

General Assembly's 2015 Data Science course in Washington, DC

A curated list of Best Artificial Intelligence Resources

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

Visualize decision trees in Python

pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks

🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!

Machine learning for C# .Net

Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.