# An Introduction to Statistical Learning

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## An Introduction to Statistical Learning

Author | : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani |

Publsiher | : Springer Science & Business Media |

Total Pages | : 426 |

Release | : 2013-06-24 |

ISBN 10 | : 1461471389 |

ISBN 13 | : 9781461471387 |

Language | : EN, FR, DE, ES & NL |

**An Introduction to Statistical Learning Book Review:**

An Introduction to Statistical Learning 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. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. 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. 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. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

## An Introduction to Statistical Learning

Author | : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani |

Publsiher | : Springer |

Total Pages | : 426 |

Release | : 2014-07-11 |

ISBN 10 | : 9781461471370 |

ISBN 13 | : 1461471370 |

Language | : EN, FR, DE, ES & NL |

**An Introduction to Statistical Learning Book Review:**

An Introduction to Statistical Learning 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. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. 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. 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. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

## An Introduction to Statistical Learning

Author | : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani |

Publsiher | : Unknown |

Total Pages | : 426 |

Release | : 2021 |

ISBN 10 | : 1928374650XXX |

ISBN 13 | : OCLC:1020513587 |

Language | : EN, FR, DE, ES & NL |

**An Introduction to Statistical Learning Book Review:**

This book presents some of the most important modeling and preddición tecniques. Include linear regression, classification, resampling methods, shrinkage approaches, tress-based methods, support vector machines, clustering and more.

## The Elements of Statistical Learning

Author | : Trevor Hastie,Robert Tibshirani,Jerome Friedman |

Publsiher | : Springer Science & Business Media |

Total Pages | : 536 |

Release | : 2013-11-11 |

ISBN 10 | : 0387216065 |

ISBN 13 | : 9780387216065 |

Language | : EN, FR, DE, ES & NL |

**The Elements of Statistical Learning Book Review:**

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. This book describes the important ideas in these areas in a common conceptual framework. 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 should be 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 boosting---the 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 & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

## An Elementary Introduction to Statistical Learning Theory

Author | : Sanjeev Kulkarni,Gilbert Harman |

Publsiher | : John Wiley & Sons |

Total Pages | : 288 |

Release | : 2011-06-09 |

ISBN 10 | : 9781118023464 |

ISBN 13 | : 1118023463 |

Language | : EN, FR, DE, ES & NL |

**An Elementary Introduction to Statistical Learning Theory Book Review:**

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.

## Statistical Learning with Sparsity

Author | : Trevor Hastie,Robert Tibshirani,Martin Wainwright |

Publsiher | : CRC Press |

Total Pages | : 367 |

Release | : 2015-05-07 |

ISBN 10 | : 1498712177 |

ISBN 13 | : 9781498712170 |

Language | : EN, FR, DE, ES & NL |

**Statistical Learning with Sparsity Book Review:**

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of l1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

## Introduction to Statistical Machine Learning

Author | : Masashi Sugiyama |

Publsiher | : Morgan Kaufmann |

Total Pages | : 534 |

Release | : 2015-10-31 |

ISBN 10 | : 0128023503 |

ISBN 13 | : 9780128023501 |

Language | : EN, FR, DE, ES & NL |

**Introduction to Statistical Machine Learning Book Review:**

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. 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. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. 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. Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.

## Machine Learning and Data Science

Author | : Daniel D. Gutierrez |

Publsiher | : Technics Publications |

Total Pages | : 282 |

Release | : 2015-11-01 |

ISBN 10 | : 1634620984 |

ISBN 13 | : 9781634620987 |

Language | : EN, FR, DE, ES & NL |

**Machine Learning and Data Science Book Review:**

A practitioner’s tools have a direct impact on the success of his or her work. 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.

## The Nature of Statistical Learning Theory

Author | : Vladimir Vapnik |

Publsiher | : Springer Science & Business Media |

Total Pages | : 314 |

Release | : 2013-06-29 |

ISBN 10 | : 1475732643 |

ISBN 13 | : 9781475732641 |

Language | : EN, FR, DE, ES & NL |

**The Nature of Statistical Learning Theory Book Review:**

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.

## Statistical Learning from a Regression Perspective

Author | : Richard A. Berk |

Publsiher | : Springer Nature |

Total Pages | : 433 |

Release | : 2020-06-29 |

ISBN 10 | : 3030401898 |

ISBN 13 | : 9783030401894 |

Language | : EN, FR, DE, ES & NL |

**Statistical Learning from a Regression Perspective Book Review:**

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. The third edition considers significant advances in recent years, among which are: the development of overarching, conceptual frameworks for statistical learning; the impact of “big data” on statistical learning; the nature and consequences of post-model selection statistical inference; deep learning in various forms; the special challenges to statistical inference posed by statistical learning; the fundamental connections between data collection and data analysis; interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy. 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.

## A Computational Approach to Statistical Learning

Author | : Taylor Arnold,Michael Kane,Bryan W. Lewis |

Publsiher | : CRC Press |

Total Pages | : 362 |

Release | : 2019-01-23 |

ISBN 10 | : 1351694766 |

ISBN 13 | : 9781351694766 |

Language | : EN, FR, DE, ES & NL |

**A Computational Approach to Statistical Learning Book Review:**

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. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.

## Mathematics for Machine Learning

Author | : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong |

Publsiher | : Cambridge University Press |

Total Pages | : 398 |

Release | : 2020-03-31 |

ISBN 10 | : 1108470041 |

ISBN 13 | : 9781108470049 |

Language | : EN, FR, DE, ES & NL |

**Mathematics for Machine Learning Book Review:**

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

## Introduction to Statistical Relational Learning

Author | : Lise Getoor,Ben Taskar |

Publsiher | : MIT Press |

Total Pages | : 608 |

Release | : 2019-09-22 |

ISBN 10 | : 0262538687 |

ISBN 13 | : 9780262538688 |

Language | : EN, FR, DE, ES & NL |

**Introduction to Statistical Relational Learning Book Review:**

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.

## Neural Networks and Statistical Learning

Author | : Ke-Lin Du,M. N. S. Swamy |

Publsiher | : Springer Nature |

Total Pages | : 988 |

Release | : 2019-09-12 |

ISBN 10 | : 1447174526 |

ISBN 13 | : 9781447174523 |

Language | : EN, FR, DE, ES & NL |

**Neural Networks and Statistical Learning Book Review:**

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

## An Introduction to Statistics with Python

Author | : Thomas Haslwanter |

Publsiher | : Springer |

Total Pages | : 278 |

Release | : 2016-07-20 |

ISBN 10 | : 3319283162 |

ISBN 13 | : 9783319283166 |

Language | : EN, FR, DE, ES & NL |

**An Introduction to Statistics with Python Book Review:**

This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.

## Statistical Learning for Big Dependent Data

Author | : Daniel Peña,Ruey S. Tsay |

Publsiher | : John Wiley & Sons |

Total Pages | : 560 |

Release | : 2021-05-04 |

ISBN 10 | : 1119417384 |

ISBN 13 | : 9781119417385 |

Language | : EN, FR, DE, ES & NL |

**Statistical Learning for Big Dependent Data Book Review:**

Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time series An automatic procedure to build univariate ARMA models for individual components of a large data set Powerful outlier detection procedures for large sets of related time series New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting. Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.

## Statistical Learning and Data Science

Author | : Mireille Gettler Summa,Leon Bottou,Bernard Goldfarb,Fionn Murtagh,Catherine Pardoux,Myriam Touati |

Publsiher | : CRC Press |

Total Pages | : 243 |

Release | : 2011-12-19 |

ISBN 10 | : 143986764X |

ISBN 13 | : 9781439867648 |

Language | : EN, FR, DE, ES & NL |

**Statistical Learning and Data Science Book Review:**

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor

## Introduction to Statistical and Machine Learning Methods for Data Science

Author | : Carlos Andre Reis Pinheiro,Mike Patetta |

Publsiher | : SAS Institute |

Total Pages | : 170 |

Release | : 2021-08-06 |

ISBN 10 | : 1953329624 |

ISBN 13 | : 9781953329622 |

Language | : EN, FR, DE, ES & NL |

**Introduction to Statistical and Machine Learning Methods for Data Science Book Review:**

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.

## The Elements of Statistical Learning

Author | : Trevor Hastie,Robert Tibshirani,Jerome H. Friedman |

Publsiher | : Unknown |

Total Pages | : 745 |

Release | : 2009 |

ISBN 10 | : 9780387848846 |

ISBN 13 | : 0387848843 |

Language | : EN, FR, DE, ES & NL |

**The Elements of Statistical Learning Book Review:**

## Statistical Learning with Math and Python

Author | : Joe Suzuki |

Publsiher | : Springer Nature |

Total Pages | : 256 |

Release | : 2021-08-03 |

ISBN 10 | : 981157877X |

ISBN 13 | : 9789811578779 |

Language | : EN, FR, DE, ES & NL |

**Statistical Learning with Math and Python Book Review:**

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.