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An Introduction to Statistical Learning : with Applications in R.
An introduction to statistical learning for free#
The pdf for this book is available for free on the book website. It provides basic theoretical foundations in statistical learning for an introductory course of data science. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter. We focus on what we consider to be the important elements of modern data analysis. The chart has 1 X axis displaying Years of Experience. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This is an example of supervised learning, where we have supervising outputs (salary values) that guide us in developing a statistical model to determine the relationship between experience level and salary. This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. Th is book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learn-ing techniques to analyze their data. The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso) nonlinear models, splines and generalized additive models tree-based methods, random forests and boosting support-vector machines. This is an introductory-level course in supervised learning, with a focus on regression and classification methods.