The Elements of Statistical Learning Data Mining Inference and Prediction Second Edition Springer Se
Most Recommended Data Science and Machine Learning Books by Top Master's Programs
It seems that you're in Germany. We have a dedicated site for Germany. During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology.
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The Elements of Statistical Learning Data Mining, Inference, and Prediction Springer Series in Stati
GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This repo contains my solutions to select problems of the book 'The Elements of Statistical Learning' by Profs.
Due to the amount of time it takes to wade through degree requirements, course codes, and catalogs, this article will continue to evolve as I gather more data. This book was either the assigned textbook or recommended reading in every Masters program I researched. Just mastering ISLR is often enough for data analyst roles. Overall, ESL takes an applied, frequentist approach, as opposed to a Bayesian approach like in the next book. Exercises in this book are not only challenging, but also very useful for individuals generally interested in machine learning research.
I'm currently working through The Elements of Statistical Learning , a textbook widely regarded as one of the best ways to get a solid foundation in statistical decision theory, the mathematical underpinnings of machine learning. After starting, it became clear to me why the book has built up such a reputation! The text begins from the very basics of function approximation and rigorously works its way up to more advanced models such as random forests and neural networks. It doesn't just spew out formulae, but supplements every topic with examples and practical discussions. Best of all, the book is FREE! Still, just as with any textbook, it's not a quick and easy read.