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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.
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.
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.
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