- #PDF AN INTRODUCTION TO STATISTICAL LEARNING PDF#
- #PDF AN INTRODUCTION TO STATISTICAL LEARNING SOFTWARE#
- #PDF AN INTRODUCTION TO STATISTICAL LEARNING CODE#
Machine Learning, Neural and Statistical Classification (D.Statistical Foundations of Machine Learning (Gianluca Bontempi).Statistical Learning and Sequential Prediction.The Elements of Statistical Learning: Data Mining, Inference, etc.
#PDF AN INTRODUCTION TO STATISTICAL LEARNING PDF#
#PDF AN INTRODUCTION TO STATISTICAL LEARNING CODE#
R code has been updated throughout to ensure compatibility. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. 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. Data Science, Machine Learning, Data Mining and Statistical Learning Books - DS-ML-Books/An Introduction to Statistical Learning - Gareth James.pdf at.
#PDF AN INTRODUCTION TO STATISTICAL LEARNING SOFTWARE#
Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Color graphics and real-world examples are used to illustrate the methods presented. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. This book presents some of the most important modeling and prediction techniques, along with relevant applications. This book provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.