# krainaksiazek machine learning and data science an introduction to statistical learning methods with r 20128185

- znaleziono 16 produktów w 3 sklepach

### Introduction to Statistical Machine Learning MORGAN KAUFMANN

**Książki / Literatura obcojęzyczna**

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 tasksDiscusses 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.

Sklep: Libristo.pl

### Introduction To Statistical Machine Learning

**Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer science>Artificial intelligence>Machine learning**

Sklep: Gigant.pl

### Introduction To Statistical Machine Learning

**Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer science>Artificial intelligence>Machine learning**

Sklep: Gigant.pl

### Introduction To Statistical Machine Learning

**Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer science>Artificial intelligence>Machine learning**

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Sklep: Gigant.pl

### Machine Learning Using R APRESS L.P.

**Książki / Literatura obcojęzyczna**

This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data.This new paradigm of teaching Machine Learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in Blockchain and Capitalism makes it easy for someone to connect the dots.For every Machine Learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. In the end, readers will learn some of the latest technological advancements in building a scalable machine learning model with Big Data. Who This Book is For: Data scientists, data science professionals and researchers in academia who want to understand the nuances of Machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark. What you will learn: 1. ML model building process flow2. Theoretical aspects of Machine Learning3. Industry based Case-Study4. Example based understanding of ML algorithm using R5. Building ML models using Apache Hadoop and Spark

Sklep: Libristo.pl

### An Introduction To Support Vector Machines And Other Kernel - Based Learning Methods

**Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer science>Artificial intelligence>Machine learningKsiążki Obc...**

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Sklep: Gigant.pl

### Pattern Recognition and Machine Learning Springer

**Książki / Literatura obcojęzyczna**

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra isrequired, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.Coming soon: For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text) For instructors, worked solutions to remaining exercises from the Springer web site Lecture slides to accompany each chapter Data sets available for download

Sklep: Libristo.pl

### Categorization and Machine Learning Books on Demand

**Książki / Literatura obcojęzyczna**

Machine learning is the attempt to imitate human categorization of perceived reality in computers. It is driven by the desire to provide machines that are as open-minded, intelligent and flexible as humans. The central goal is to provide classifications for arbitrary types of input data: Labels that characterize the data correctly, given some examples. Machine learning has been a research topic of computer science for several decades. This book summarizes the major findings, explains the practically relevant methods and discusses their communalities and differences. In the first of three parts, we introduce the setting, goals and all necessary tools for the definition, application and evaluation of learning algorithms. The second part discusses and compares the various algorithms employed in machine categorization today. We structure them in four groups: the optimization algorithms, risk minimization approaches, those that employ probabilistic inference and those that imitate neural inference processes. Outstanding examples from the list of algorithms are the vector space mode, the support vector machine, Bayes and Markov processes, conditional random fields, radial basis function networks and methods employed for deep learning such as the Boltzmann machine. The third part reviews the algorithms and explores the theoretical frontiers of machine learning. In summary, we endeavor to provide a comprehensive yet intuitive introduction into the field of categorization. Neither parallels to human cognition are neglected nor recent developments in algorithm design or theoretical justification. As a research field, machine learning is gaining more and more attention. This book explains what it is, where it can be applied and how it is done.

Sklep: Libristo.pl

### MACHINE LEARNING FOR MARKETERS John Wiley & Sons Inc

**Książki / Literatura obcojęzyczna**

A straightforward, non-technical guide to the next major marketing tool Artificial Intelligence for Marketing presents a tightly-focused introduction to machine learning, written specifically for marketing professionals. This book will not teach you to be a data scientist--but it does explain how Artificial Intelligence and Machine Learning will revolutionize your company's marketing strategy, and teach you how to use it most effectively. Data and analytics have become table stakes in modern marketing, but the field is ever-evolving with data scientists continually developing new algorithms--where does that leave you? How can marketers use the latest data science developments to their advantage? This book walks you through the "need-to-know" aspects of Artificial Intelligence, including natural language processing, speech recognition, and the power of Machine Learning to show you how to make the most of this technology in a practical, tactical way. Simple illustrations clarify complex concepts, and case studies show how real-world companies are taking the next leap forward. Straightforward, pragmatic, and with no math required, this book will help you: Speak intelligently about Artificial Intelligence and its advantages in marketing Understand how marketers without a Data Science degree can make use of machine learning technology Collaborate with data scientists as a subject matter expert to help develop focused-use applications Help your company gain a competitive advantage by leveraging leading-edge technology in marketing Marketing and data science are two fast-moving, turbulent spheres that often intersect; that intersection is where marketing professionals pick up the tools and methods to move their company forward. Artificial Intelligence and Machine Learning provide a data-driven basis for more robust and intensely-targeted marketing strategies--and companies that effectively utilize these latest tools will reap the benefit in the marketplace. Artificial Intelligence for Marketing provides a nontechnical crash course to help you stay ahead of the curve.

Sklep: Libristo.pl

### Mathematical Problems in Data Science Springer, Berlin

**Książki / Literatura obcojęzyczna**

This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark.§§This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, mani§fold learning, business and financial data recovery, geometric search, and computing models.§§Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.§

Sklep: Libristo.pl

### Ensemble Methods in Data Mining Morgan & Claypool Publishers

**Książki / Literatura obcojęzyczna**

This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners.

Sklep: Libristo.pl

### R for Marketing Research and Analytics Springer, Berlin

**Książki / Literatura obcojęzyczna**

This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis.§§Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis.§§With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.§

Sklep: Libristo.pl

### Intelligent Information Processing and Web Mining Springer, Berlin

**Książki / Literatura obcojęzyczna**

This volume contains selected papers, presented at the international conference on Intelligent Information Processing and Web Mining Conference IIS:IIPWM'06, organized in Ustro (Poland), 2006. The submitted papers cover new computing paradigms, among others in biologically motivated methods, advanced data analysis, new machine learning paradigms, natural language processing, new optimization technologies, applied data mining using statistical and non-standard approaches.§ This volume contains selected papers, presented at the international conference on Intelligent Information Processing and Web Mining Conference IIS:IIPWM'06, organized in Ustro" (Poland) on June 19-22nd, 2006. The submitted papers cover new computing paradigms, among others in biologically motivated methods, advanced data analysis, new machine learning paradigms, natural language processing, new optimization technologies, applied data mining using statistical and non-standard approaches. The papers give an overview over a wide range of applications for intelligent systems: in operating systems design, in network security, for information extraction from multimedia (sound, graphics), in financial market analysis, in medicine, in geo-science, etc. This volume of VI IIS:IIPWM'06 Proceeding will be a valuable reference work in further research for computer scientists, mathematicians, engineers, logicians and other interested researchers who find excitement in advancing the area of intelligent systems.

Sklep: Libristo.pl

### Survival Analysis Springer

**Książki / Literatura obcojęzyczna**

This greatly expanded third edition of Survival Analysis- A Self-learning Text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. This text is suitable for researchers and statisticians working in the medical and other life sciences as well as statisticians in academia who teach introductory and second-level courses on survival analysis. §The third edition continues to use the unique "lecture-book" format of the first two editions with one new chapter, additional sections and clarifications to several chapters, and a revised computer appendix. The Computer Appendix, with step-by-step instructions for using the computer packages STATA, SAS, and SPSS, is expanded to include the software package R.§David Kleinbaum is Professor of Epidemiology at the Rollins School of Public Health at Emory University, Atlanta, Georgia. Dr. Kleinbaum is internationally known for innovative textbooks and teaching on epidemiological methods, multiple linear regression, logistic regression, and survival analysis. He has provided extensive worldwide short-course training in over 150 short courses on statistical and epidemiological methods. He is also the author of ActivEpi (2002), an interactive computer-based instructional text on fundamentals of epidemiology, which has been used in a variety of educational environments including distance learning. §Mitchel Klein is Research Assistant Professor with a joint appointment in the Department of Environmental and Occupational Health (EOH) and the Department of Epidemiology, also at the Rollins School of Public Health at Emory University. Dr. Klein is also co-author with Dr. Kleinbaum of the second edition of Logistic Regression- A Self-Learning Text (2002). He has regularly taught epidemiologic methods courses at Emory to graduate students in public health and in clinical medicine. He is responsible for the epidemiologic methods training of physicians enrolled in Emory s Master of Science in Clinical Research Program, and has collaborated with Dr. Kleinbaum both nationally and internationally in teaching several short courses on various topics in epidemiologic methods.

Sklep: Libristo.pl

### Revolution Radio - Green Day (Płyta CD)

**Książki & Multimedia > Muzyka**

Opis - 12 studyjny album Green Day zatytułowany

Sklep: InBook.pl

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