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## Data Mining and Machine Learning

Author | : Mohammed J. Zaki,Wagner Meira, Jr |

Publsiher | : Cambridge University Press |

Total Pages | : 775 |

Release | : 2020-01-31 |

ISBN 10 | : 1108473989 |

ISBN 13 | : 9781108473989 |

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

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

New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

## R and Data Mining

Author | : Yanchang Zhao |

Publsiher | : Academic Press |

Total Pages | : 256 |

Release | : 2012-12-31 |

ISBN 10 | : 012397271X |

ISBN 13 | : 9780123972712 |

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

**R and Data Mining Book Review:**

R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation. With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work

## Mining of Massive Datasets

Author | : Jure Leskovec,Anand Rajaraman,Jeffrey David Ullman |

Publsiher | : Cambridge University Press |

Total Pages | : 476 |

Release | : 2014-11-13 |

ISBN 10 | : 1107077230 |

ISBN 13 | : 9781107077232 |

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

**Mining of Massive Datasets Book Review:**

Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

## Data Science for Business

Author | : Foster Provost,Tom Fawcett |

Publsiher | : "O'Reilly Media, Inc." |

Total Pages | : 414 |

Release | : 2013-07-27 |

ISBN 10 | : 144937428X |

ISBN 13 | : 9781449374280 |

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

**Data Science for Business Book Review:**

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

## Data Mining with Rattle and R

Author | : Graham Williams |

Publsiher | : Springer Science & Business Media |

Total Pages | : 374 |

Release | : 2011-08-04 |

ISBN 10 | : 144199890X |

ISBN 13 | : 9781441998903 |

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

**Data Mining with Rattle and R Book Review:**

Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.

## Learn Data Mining Through Excel

Author | : Hong Zhou |

Publsiher | : Apress |

Total Pages | : 219 |

Release | : 2020-06-13 |

ISBN 10 | : 1484259823 |

ISBN 13 | : 9781484259825 |

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

**Learn Data Mining Through Excel Book Review:**

Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are conducting your mining task, offering a deeper understanding of how data is manipulated and results are obtained. These are critical aspects of the model construction process that are hidden in software tools and programming language packages. This book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data. What You Will Learn Comprehend data mining using a visual step-by-step approach Build on a theoretical introduction of a data mining method, followed by an Excel implementation Unveil the mystery behind machine learning algorithms, making a complex topic accessible to everyone Become skilled in creative uses of Excel formulas and functions Obtain hands-on experience with data mining and Excel Who This Book Is For Anyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel, who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended.

## Data Mining with R

Author | : Luis Torgo |

Publsiher | : CRC Press |

Total Pages | : 426 |

Release | : 2016-11-30 |

ISBN 10 | : 1315399091 |

ISBN 13 | : 9781315399096 |

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

**Data Mining with R Book Review:**

Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.

## Data Jujitsu

Author | : Anonim |

Publsiher | : "O'Reilly Media, Inc." |

Total Pages | : 135 |

Release | : 2012 |

ISBN 10 | : 1449341152 |

ISBN 13 | : 9781449341152 |

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

**Data Jujitsu Book Review:**

## Text Mining with R

Author | : Julia Silge,David Robinson |

Publsiher | : "O'Reilly Media, Inc." |

Total Pages | : 194 |

Release | : 2017-06-12 |

ISBN 10 | : 1491981628 |

ISBN 13 | : 9781491981627 |

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

**Text Mining with R Book Review:**

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document’s most important terms with frequency measurements Explore relationships and connections between words with the ggraph and widyr packages Convert back and forth between R’s tidy and non-tidy text formats Use topic modeling to classify document collections into natural groups Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages

## Introduction to Algorithms for Data Mining and Machine Learning

Author | : Xin-She Yang |

Publsiher | : Academic Press |

Total Pages | : 188 |

Release | : 2019-06-17 |

ISBN 10 | : 0128172177 |

ISBN 13 | : 9780128172179 |

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

**Introduction to Algorithms for Data Mining and Machine Learning Book Review:**

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

## Social Media Mining

Author | : Reza Zafarani,Mohammad Ali Abbasi,Huan Liu |

Publsiher | : Cambridge University Press |

Total Pages | : 320 |

Release | : 2014-04-28 |

ISBN 10 | : 1107018854 |

ISBN 13 | : 9781107018853 |

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

**Social Media Mining Book Review:**

Integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining.

## Data Science and Machine Learning

Author | : Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman |

Publsiher | : CRC Press |

Total Pages | : 510 |

Release | : 2019-11-20 |

ISBN 10 | : 1000730778 |

ISBN 13 | : 9781000730777 |

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

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

"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code. I only wish a book like this had been around when I first began my journey!" -Nicholas Hoell, University of Toronto "This is a well-written book that provides a deeper dive into data-scientific methods than many introductory texts. The writing is clear, and the text logically builds up regularization, classification, and decision trees. Compared to its probable competitors, it carves out a unique niche. -Adam Loy, Carleton College The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science. Key Features: Focuses on mathematical understanding. Presentation is self-contained, accessible, and comprehensive. Extensive list of exercises and worked-out examples. Many concrete algorithms with Python code. Full color throughout. The Authors: Dirk P. Kroese, PhD, is a Professor of Mathematics and Statistics at The University of Queensland. He has published over 120 articles and five books in a wide range of areas in mathematics, statistics, data science, machine learning, and Monte Carlo methods. He is a pioneer of the well-known Cross-Entropy method—an adaptive Monte Carlo technique, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance. Zdravko Botev, PhD, is an Australian Mathematical Science Institute Lecturer in Data Science and Machine Learning with an appointment at the University of New South Wales in Sydney, Australia. He is the recipient of the 2018 Christopher Heyde Medal of the Australian Academy of Science for distinguished research in the Mathematical Sciences. Thomas Taimre, PhD, is a Senior Lecturer of Mathematics and Statistics at The University of Queensland. His research interests range from applied probability and Monte Carlo methods to applied physics and the remarkably universal self-mixing effect in lasers. He has published over 100 articles, holds a patent, and is the coauthor of Handbook of Monte Carlo Methods (Wiley). Radislav Vaisman, PhD, is a Lecturer of Mathematics and Statistics at The University of Queensland. His research interests lie at the intersection of applied probability, machine learning, and computer science. He has published over 20 articles and two books.

## 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.

## Learning Data Mining with Python

Author | : Robert Layton |

Publsiher | : Packt Publishing Ltd |

Total Pages | : 344 |

Release | : 2015-07-29 |

ISBN 10 | : 1784391204 |

ISBN 13 | : 9781784391201 |

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

**Learning Data Mining with Python Book Review:**

The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems. There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.

## Data Mining

Author | : John Wang |

Publsiher | : IGI Global |

Total Pages | : 468 |

Release | : 2003-01-01 |

ISBN 10 | : 9781591400516 |

ISBN 13 | : 1591400511 |

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

**Data Mining Book Review:**

Data Mining: Opportunities and Challenges presents an overview of the state of the art approaches in this new and multidisciplinary field of data mining. The primary objective of this book is to explore the myriad issues regarding data mining, specifically focusing on those areas that explore new methodologies or examine case studies. This book contains numerous chapters written by an international team of forty-four experts representing leading scientists and talented young scholars from seven different countries.

## Transparent Data Mining for Big and Small Data

Author | : Tania Cerquitelli,Daniele Quercia,Frank Pasquale |

Publsiher | : Springer |

Total Pages | : 215 |

Release | : 2017-05-09 |

ISBN 10 | : 3319540246 |

ISBN 13 | : 9783319540245 |

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

**Transparent Data Mining for Big and Small Data Book Review:**

This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches.As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to practical use.

## Data Mining and Predictive Analytics

Author | : Daniel T. Larose |

Publsiher | : John Wiley & Sons |

Total Pages | : 824 |

Release | : 2015-02-19 |

ISBN 10 | : 1118868676 |

ISBN 13 | : 9781118868676 |

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

**Data Mining and Predictive Analytics Book Review:**

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

## Data Mining and Business Analytics with R

Author | : Johannes Ledolter |

Publsiher | : John Wiley & Sons |

Total Pages | : 368 |

Release | : 2013-05-28 |

ISBN 10 | : 1118572157 |

ISBN 13 | : 9781118572153 |

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

**Data Mining and Business Analytics with R Book Review:**

Collecting, analyzing, and extracting valuable information froma large amount of data requires easily accessible, robust,computational and analytical tools. Data Mining and BusinessAnalytics with R utilizes the open source software R for theanalysis, exploration, and simplification of large high-dimensionaldata sets. As a result, readers are provided with the neededguidance to model and interpret complicated data and become adeptat building powerful models for prediction and classification. Highlighting both underlying concepts and practicalcomputational skills, Data Mining and Business Analytics withR begins with coverage of standard linear regression and theimportance of parsimony in statistical modeling. The book includesimportant topics such as penalty-based variable selection (LASSO);logistic regression; regression and classification trees;clustering; principal components and partial least squares; and theanalysis of text and network data. In addition, the bookpresents: • A thorough discussion and extensive demonstration of thetheory behind the most useful data mining tools • Illustrations of how to use the outlined concepts inreal-world situations • Readily available additional data sets and related Rcode allowing readers to apply their own analyses to the discussedmaterials • Numerous exercises to help readers with computing skillsand deepen their understanding of the material Data Mining and Business Analytics with R is an excellentgraduate-level textbook for courses on data mining and businessanalytics. The book is also a valuable reference for practitionerswho collect and analyze data in the fields of finance, operationsmanagement, marketing, and the information sciences.

## Python for Data Analysis

Author | : Wes McKinney |

Publsiher | : "O'Reilly Media, Inc." |

Total Pages | : 550 |

Release | : 2017-09-25 |

ISBN 10 | : 1491957611 |

ISBN 13 | : 9781491957615 |

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

**Python for Data Analysis Book Review:**

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

## Introduction to Data Science

Author | : Rafael A. Irizarry |

Publsiher | : CRC Press |

Total Pages | : 713 |

Release | : 2019-11-20 |

ISBN 10 | : 1000708039 |

ISBN 13 | : 9781000708035 |

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

**Introduction to Data Science Book Review:**

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.