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.

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

🍊 📊 💡 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.

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.

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

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

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

Visualize decision trees in Python

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

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

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

🔥🌟《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!