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An Introduction to Statistical Learning with Applications in Python (statlearning.com)

447 points by alexmolas · 1146 days ago · 84 comments on HN

Article summary

An Introduction to Statistical Learning with Applications in Python is a book that provides a broad and less technical treatment of key topics in statistical learning. The book covers various topics such as regression, classification, and deep learning, and includes labs at the end of each chapter to demonstrate the concepts in Python. The book is written by leading educators in statistical learning and is suitable for anyone who wishes to use contemporary tools for data analysis. A new edition of the book with applications in Python was published in 2023.

Main themes

  • statistical learning
  • machine learning
  • Python programming
  • data analysis
  • classical ML vs deep learning
  • interpretability and simplicity
  • mathematical foundations of ML
  • hype and overreliance on LLMs and ChatGPT

What commenters say

  • Some commenters highly recommend the book for learning the theoretical aspects of classical machine learning.
  • There is a need for a Julia version of the book, but it is unclear if one will be published soon.
  • Classical machine learning methods are still very helpful and easier to interpret than some newer methods, such as RNNs.
  • The book is a key resource for getting started with machine learning and AI, but some commenters believe that the field is currently overhyped.
  • Some practitioners prefer to use simple, interpretable models like SVM or random forest over more complex models like LLMs.
  • Others argue that for many use cases, calling a ChatGPT API is a better approach than creating a custom ML model, and does not require understanding the underlying math.
  • The math behind LLMs is not as complex as some make it out to be, but others disagree and point out that it involves a range of mathematical concepts beyond just calculus and linear algebra.
  • The rise of LLMs and ChatGPT has led to a lack of understanding of the underlying concepts and a reliance on black box models, which can be problematic in practice.