We DO NOT spam and do not allow others access to your private information.The textbook was developed for OpenStax College as part of its Open Educational Resources initiative.If interested in picking up elementary statistical learning concepts, and learning how to implement them in R, this book is for you.
The book, a staple of statistical learning texts, is accessible to readers of all levels, and can be read without much of an existing foundational knowledge in the area. This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist. Another major difference between these 2 titles, beyond the level of depth of the material covered, is that ISLR introduces these topics alongside practical implementations in a programming language, in this case R. We consider ESL to be an important companion for professionals (with graduate degrees in statistics, machine learning, or related fields) who need to understand the technical details behind statistical learning approaches. However, the community of users of statistical learning techniques has expanded to include individuals with a wider range of interests and backgrounds. Therefore, we believe that there is now a place for a less technical and more accessible version of ESL. It should be apparent from the website and book excerpts and table of contents above (and perhaps even the title) that this book focuses on the practical. Already have a good understanding of classification concepts, but want to implement them using R This books for you. Want to learn about implementing linear models in R This books for you. Interested in effectively implement support vector machines using R Again, this books for you. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. ![]() Anyone who wants to intelligently analyze complex data should own this book. Its thorough, lively, written at level appropriate for undergraduates and usable by nonexperts. Its chock full of interesting examples of how modern predictive machine learning algorithms work (and dont work) in a variety of settings. This book is a great introduction to the theoretical aspect of machine learning. In case you are a Python developer, and are deterred by the use of R, you should reconsider, as R is only used for the practical examples at the end of each chapter. Furthermore, there are Python versions of those examples in the following Github repository. Introduction To Sociology Ebooks Free MIT CoursesMath for Programmers Spam Filter in Python: Naive Bayes from Scratch 5 Innovative AI Software Companies You Should Know Free MIT Courses on Calculus: The Key to Understanding. KDnuggets 20:n26, Jul 8: Speed up Your Numpy and Pandas; A.
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