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An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis. \n\nThe first edition of this book, with applications in R (ISLR), was released in 2013. A 2nd Edition of ISLR was published in 2021. It has been translated into Chinese, Italian, Japanese, Korean, Mongolian, Russian, and Vietnamese. The Python edition (ISLP) was published in 2023.\n\nEach edition contains a lab at the end of each chapter, which demonstrates the chapter’s concepts in either R or Python.\"\n\nTOpics include  linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkarimabousselham%2Fislp-applied-solutions","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkarimabousselham%2Fislp-applied-solutions","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkarimabousselham%2Fislp-applied-solutions/lists"}