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
This resource provides practical implementations and detailed explanations of advanced statistical learning algorithms. It takes complex mathematical concepts and illustrates them with code examples, proofs, and summaries. Data scientists, machine learning engineers, and statisticians can use these notebooks to deepen their understanding of foundational models and techniques for data analysis and prediction.
426 stars.
Use this if you are a data professional or student who wants to understand and apply statistical learning algorithms from a well-respected textbook through hands-on coding examples and clear explanations.
Not ideal if you are looking for a plug-and-play solution for a specific data analysis task without delving into the underlying mathematical and algorithmic details.
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426
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Jupyter Notebook
License
MIT
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Last pushed
Feb 10, 2026
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