A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning
This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.
A collection of research papers on decision, classification and regression trees with implementations.
[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Teaching Materials for Dr. Waleed A. Yousef
A comprehensive library for machine learning and numerical computing. Apply Machine Learning with Rust leveraging first principles.
A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book
Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"
My book list
An extensible framework for geospatial data science and geostatistical modeling fully written in Julia
An Introduction to Statistical Learning with Applications in PYTHON
Machine Learning library for the web and Node.
My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman
Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels
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