کتاب مقدمه‌ای بر یادگیری آماری: با کاربردهایی در پایتون

An Introduction to Statistical Learning, With Applications in R (ISLR)— first published in 2013, with a second edition in 2021 — arose fromthe clear need for a broader and less technical treatment of the key topicsin statistical learning. In addition to a review of linear regression, ISLRcovers many of today’s most important statistical and machine learningapproaches, including resampling, sparse methods for classification and regression,generalized additive models, tree-based methods, support vectormachines, deep learning, survival analysis, clustering, and multiple testing.In recent years Python has become an increasingly popular languagefor data science, and there has been increasing demand for a PythonLearning, With Applications in Python (ISLP), covers the same materialsas ISLR but with labs implemented in Python — a feat accomplished by theaddition of a new co-author, Jonathan Taylor. Several of the labs make useof the ISLP Python package, which we have written to facilitate carrying outthe statistical learning methods covered in each chapter in Python. Theselabs will be useful both for Python novices, as well as experienced users.The intention behind ISLP (and ISLR) is to concentrate more on theapplications of the methods and less on the mathematical details, so it isappropriate for advanced undergraduates or master’s students in statisticsor related quantitative fields, or for individuals in other disciplines whowish to use statistical learning tools to analyze their data. It can be usedas a textbook for a course spanning two semesters.