Five years ago, in What is Web 2. Why do we suddenly care about statistics and about data? This report examines the many sides of data science - the technologies, the co Python Data Science Handbook.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. R is one of the most popular, powerful data analytics languages and environments in use by data scientists. Model-based Bayesian methods for estimating cell proportions in cross-classification tables having ordered categories with C.
A tutorial on modeling ordered categorical response data, Psychological Bulletin , , A survey of models for repeated ordered categorical response data, Statistics in Medicine , 8 , Exact inference for contingency tables with ordered categories with C. Mehta and N. Patel , Journal of the American Statistical Association , 85 , Analysis of sparse repeated categorical measurement data with S.
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Rao and G. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning prediction to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boostingthe first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.
They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Skip to main content Skip to table of contents.
Advertisement Hide. Machine learning allows computers to learn and discern patterns without actually being programmed. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice.
Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice.
Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus. Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning. Exploring topics that are not often covered in introductory level books on statistical learning theory, including PAC learning, VC dimension, and simplicity, the authors present upper-undergraduate and graduate levels with the basic theory behind contemporary machine learning and uniquely suggest it serves as an excellent framework for philosophical thinking about inductive inference"--Back cover.
This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency.
This book can be viewed as a set of essential tools we need for a long-term career in the data science field — recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. The R statistical environment was chosen for use in this book.
R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used. This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins.
Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis.
Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications. The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture.
This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research.
Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data.
Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques.
Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions.
These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.
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