# Statistic and Probability

* [Statistics 110: Probability](https://projects.iq.harvard.edu/stat110/home)
  * [Stastitics 110 free book - Google Drive](https://drive.google.com/file/d/1VmkAAGOYCTORq1wxSQqy255qLJjTNvBI/view)
  * [Introduction to Probability - edX](https://www.edx.org/course/introduction-to-probability)
  * [Statistics 110: Probability - YouTube](https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo)
  * [Introduction to Probability - HarvardX on edX - YouTube](https://www.youtube.com/watch?v=gJZYgLyjyIQ\&list=PL2qHyNjtf9vO5fAiRKlBlXksc4B5TK_F0)
* [Book 0: “Machine Learning: A Probabilistic Perspective” (2012) - pml-book](https://probml.github.io/pml-book/)
* [Introduction to Machine Learning, Fourth Edition - The MIT Press](https://mitpress.mit.edu/books/introduction-machine-learning-fourth-edition)
* [An Introduction to Statistical Learning with Applications in R](https://faculty.marshall.usc.edu/gareth-james/ISL/)
  * [Python Version](https://github.com/JWarmenhoven/ISLR-python)
  * [Python version](https://github.com/tdpetrou/Machine-Learning-Books-With-Python/tree/master/Introduction%20to%20Statistical%20Learning)
  * [MOOC](https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about)
  * [melling/ISLR: Introduction to Statistical Learning](https://github.com/melling/ISLR)
* [Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.](https://web.stanford.edu/~hastie/ElemStatLearn/)
  * [maitbayev/the-elements-of-statistical-learning: My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman](https://github.com/maitbayev/the-elements-of-statistical-learning)
  * [EDx](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)
* [Probabilistic Machine Learning Series](https://probml.github.io/pml-book/)
  * Book 0: [Machine Learning: A Probabilistic Perspective, (2012)](https://probml.github.io/pml-book/book0.html)
  * Book 1: [Probabilistic Machine Learning: An Introduction, (2021)](https://probml.github.io/pml-book/book1.html)
  * Book 2: [Probabilistic Machine Learning: Advanced Topics, (2022)](https://probml.github.io/pml-book/book2.html)


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