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

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

A python library for decision tree visualization and model interpretation.

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

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 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

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

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

Machine learning for C# .Net

Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

🔥🌟《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 Lectures at the European Space Agency (ESA) in 2018