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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

🍊 :bar_chart: :bulb: 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.

A collection of research papers on decision, classification and regression trees with implementations.

Text Classification Algorithms: A Survey

For extensive instructor led learning