Unsupervised Learning with R

Author: Erik Rodriguez Pacheco

Publisher: Packt Publishing Ltd

ISBN: 1785885812

Category: Computers

Page: 192

View: 4300

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Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data About This Book Unlock and discover how to tackle clusters of raw data through practical examples in R Explore your data and create your own models from scratch Analyze the main aspects of unsupervised learning with this comprehensive, practical step-by-step guide Who This Book Is For This book is intended for professionals who are interested in data analysis using unsupervised learning techniques, as well as data analysts, statisticians, and data scientists seeking to learn to use R to apply data mining techniques. Knowledge of R, machine learning, and mathematics would help, but are not a strict requirement. What You Will Learn Load, manipulate, and explore your data in R using techniques for exploratory data analysis such as summarization, manipulation, correlation, and data visualization Transform your data by using approaches such as scaling, re-centering, scale [0-1], median/MAD, natural log, and imputation data Build and interpret clustering models using K-Means algorithms in R Build and interpret clustering models by Hierarchical Clustering Algorithm's in R Understand and apply dimensionality reduction techniques Create and use learning association rules models, such as recommendation algorithms Use and learn about the techniques of feature selection Install and use end-user tools as an alternative to programming directly in the R console In Detail The R Project for Statistical Computing provides an excellent platform to tackle data processing, data manipulation, modeling, and presentation. The capabilities of this language, its freedom of use, and a very active community of users makes R one of the best tools to learn and implement unsupervised learning. If you are new to R or want to learn about unsupervised learning, this book is for you. Packed with critical information, this book will guide you through a conceptual explanation and practical examples programmed directly into the R console. Starting from the beginning, this book introduces you to unsupervised learning and provides a high-level introduction to the topic. We quickly move on to discuss the application of key concepts and techniques for exploratory data analysis. The book then teaches you to identify groups with the help of clustering methods or building association rules. Finally, it provides alternatives for the treatment of high-dimensional datasets, as well as using dimensionality reduction techniques and feature selection techniques. By the end of this book, you will be able to implement unsupervised learning and various approaches associated with it in real-world projects. Style and approach This book takes a step-by-step approach to unsupervised learning concepts and tools, explained in a conversational and easy-to-follow style. Each topic is explained sequentially, explaining the theory and then putting it into practice by using specialized R packages for each topic.

Mastering Machine Learning with R

Author: Cory Lesmeister

Publisher: Packt Publishing Ltd

ISBN: 1787284484

Category: Computers

Page: 420

View: 1210

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Master machine learning techniques with R to deliver insights in complex projects About This Book Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised Learning Implement advanced concepts in machine learning with this example-rich guide Who This Book Is For This book is for data science professionals, data analysts, or anyone with a working knowledge of machine learning, with R who now want to take their skills to the next level and become an expert in the field. What You Will Learn Gain deep insights into the application of machine learning tools in the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Master fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Learn how to use R in a cloud service such as Amazon In Detail This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you'll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets. Style and approach The book delivers practical and real-world solutions to problems and a variety of tasks such as complex recommendation systems. By the end of this book, you will have gained expertise in performing R machine learning and will be able to build complex machine learning projects using R and its packages.

Introduction to Machine Learning with R

Rigorous Mathematical Analysis

Author: Scott V. Burger

Publisher: "O'Reilly Media, Inc."

ISBN: 149197639X

Category: Computers

Page: 226

View: 374

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Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods. Finally, you’ll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification models, you’ll be able to solve an array of machine learning problems. Author Scott V. Burger provides several examples to help you build a working knowledge of machine learning. Explore machine learning models, algorithms, and data training Understand machine learning algorithms for supervised and unsupervised cases Examine statistical concepts for designing data for use in models Dive into linear regression models used in business and science Use single-layer and multilayer neural networks for calculating outcomes Look at how tree-based models work, including popular decision trees Get a comprehensive view of the machine learning ecosystem in R Explore the powerhouse of tools available in R’s caret package

Practical Guide to Cluster Analysis in R

Unsupervised Machine Learning

Author: Alboukadel Kassambara

Publisher: STHDA

ISBN: 1542462703

Category: Cluster analysis

Page: 187

View: 4642

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Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. Part I provides a quick introduction to R and presents required R packages, as well as, data formats and dissimilarity measures for cluster analysis and visualization. Part II covers partitioning clustering methods, which subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. Partitioning clustering approaches include: K-means, K-Medoids (PAM) and CLARA algorithms. In Part III, we consider hierarchical clustering method, which is an alternative approach to partitioning clustering. The result of hierarchical clustering is a tree-based representation of the objects called dendrogram. In this part, we describe how to compute, visualize, interpret and compare dendrograms. Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Among the chapters covered here, there are: Assessing clustering tendency, Determining the optimal number of clusters, Cluster validation statistics, Choosing the best clustering algorithms and Computing p-value for hierarchical clustering. Part V presents advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering.

Neural Networks with R

Smart models using CNN, RNN, deep learning, and artificial intelligence principles

Author: Giuseppe Ciaburro,Balaji Venkateswaran

Publisher: Packt Publishing Ltd

ISBN: 1788399412

Category: Computers

Page: 270

View: 6253

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Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.

An Introduction to Statistical Learning

with Applications in R

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

Publisher: Springer Science & Business Media

ISBN: 1461471389

Category: Mathematics

Page: 426

View: 6966

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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.

Machine Learning with R

Author: Abhijit Ghatak

Publisher: Springer

ISBN: 9811068089

Category: Computers

Page: 210

View: 6163

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This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it’s applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning. In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation. The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.

Statistics for Machine Learning

Author: Pratap Dangeti

Publisher: Packt Publishing Ltd

ISBN: 1788291220

Category: Computers

Page: 442

View: 4341

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Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.

R Deep Learning Essentials

Author: Dr. Joshua F. Wiley

Publisher: Packt Publishing Ltd

ISBN: 1785284711

Category: Computers

Page: 170

View: 4409

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Build automatic classification and prediction models using unsupervised learning About This Book Harness the ability to build algorithms for unsupervised data using deep learning concepts with R Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models Build models relating to neural networks, prediction and deep prediction Who This Book Is For This book caters to aspiring data scientists who are well versed with machine learning concepts with R and are looking to explore the deep learning paradigm using the packages available in R. You should have a fundamental understanding of the R language and be comfortable with statistical algorithms and machine learning techniques, but you do not need to be well versed with deep learning concepts. What You Will Learn Set up the R package H2O to train deep learning models Understand the core concepts behind deep learning models Use Autoencoders to identify anomalous data or outliers Predict or classify data automatically using deep neural networks Build generalizable models using regularization to avoid overfitting the training data In Detail Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning. This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples. After installing the H2O package, you will learn about prediction algorithms. Moving ahead, concepts such as overfitting data, anomalous data, and deep prediction models are explained. Finally, the book will cover concepts relating to tuning and optimizing models. Style and approach This book takes a practical approach to showing you the concepts of deep learning with the R programming language. We will start with setting up important deep learning packages available in R and then move towards building models related to neural network, prediction, and deep prediction - and all of this with the help of real-life examples.

Machine Learning with R

Author: Brett Lantz

Publisher: Packt Publishing Ltd

ISBN: 1782162151

Category: Computers

Page: 396

View: 8350

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Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

Advanced Analytics with R and Tableau

Author: Jen Stirrup,Ruben Oliva Ramos

Publisher: Packt Publishing Ltd

ISBN: 1786460246

Category: Computers

Page: 178

View: 6374

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Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of R About This Book A comprehensive guide that will bring out the creativity in you to visualize the results of complex calculations using Tableau and R Combine Tableau analytics and visualization with the power of R using this step-by-step guide Wondering how R can be used with Tableau? This book is your one-stop solution. Who This Book Is For This book will appeal to Tableau users who want to go beyond the Tableau interface and deploy the full potential of Tableau, by using R to perform advanced analytics with Tableau. A basic familiarity with R is useful but not compulsory, as the book will start off with concrete examples of R and will move quickly into more advanced spheres of analytics using online data sources to support hands-on learning. Those R developers who want to integrate R in Tableau will also benefit from this book. What You Will Learn Integrate Tableau's analytics with the industry-standard, statistical prowess of R. Make R function calls in Tableau, and visualize R functions with Tableau using RServe. Use the CRISP-DM methodology to create a roadmap for analytics investigations. Implement various supervised and unsupervised learning algorithms in R to return values to Tableau. Make quick, cogent, and data-driven decisions for your business using advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau/R calculated field functions. In Detail Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics. By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples. Style and approach Tableau (uniquely) offers excellent visualization combined with advanced analytics; R is at the pinnacle of statistical computational languages. When you want to move from one view of data to another, backed up by complex computations, the combination of R and Tableau makes the perfect solution. This example-rich guide will teach you how to combine these two to perform advanced analytics by integrating Tableau with R and create beautiful data visualizations.

Practical Machine Learning with H2O

Powerful, Scalable Techniques for Deep Learning and AI

Author: Darren Cook

Publisher: "O'Reilly Media, Inc."

ISBN: 1491964553

Category: Computers

Page: 300

View: 4139

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Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that’s easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you’re familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You’ll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning. Learn how to import, manipulate, and export data with H2O Explore key machine-learning concepts, such as cross-validation and validation data sets Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification Use H2O to analyze each sample data set with four supervised machine-learning algorithms Understand how cluster analysis and other unsupervised machine-learning algorithms work

Learning Bayesian Models with R

Author: Dr. Hari M. Koduvely

Publisher: Packt Publishing Ltd

ISBN: 1783987618

Category: Computers

Page: 168

View: 5284

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Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems About This Book Understand the principles of Bayesian Inference with less mathematical equations Learn state-of-the art Machine Learning methods Familiarize yourself with the recent advances in Deep Learning and Big Data frameworks with this step-by-step guide Who This Book Is For This book is for statisticians, analysts, and data scientists who want to build a Bayes-based system with R and implement it in their day-to-day models and projects. It is mainly intended for Data Scientists and Software Engineers who are involved in the development of Advanced Analytics applications. To understand this book, it would be useful if you have basic knowledge of probability theory and analytics and some familiarity with the programming language R. What You Will Learn Set up the R environment Create a classification model to predict and explore discrete variables Get acquainted with Probability Theory to analyze random events Build Linear Regression models Use Bayesian networks to infer the probability distribution of decision variables in a problem Model a problem using Bayesian Linear Regression approach with the R package BLR Use Bayesian Logistic Regression model to classify numerical data Perform Bayesian Inference on massively large data sets using the MapReduce programs in R and Cloud computing In Detail Bayesian Inference provides a unified framework to deal with all sorts of uncertainties when learning patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world problems requires high computational resources. With the recent advances in computation and several open sources packages available in R, Bayesian modeling has become more feasible to use for practical applications today. Therefore, it would be advantageous for all data scientists and engineers to understand Bayesian methods and apply them in their projects to achieve better results. Learning Bayesian Models with R starts by giving you a comprehensive coverage of the Bayesian Machine Learning models and the R packages that implement them. It begins with an introduction to the fundamentals of probability theory and R programming for those who are new to the subject. Then the book covers some of the important machine learning methods, both supervised and unsupervised learning, implemented using Bayesian Inference and R. Every chapter begins with a theoretical description of the method explained in a very simple manner. Then, relevant R packages are discussed and some illustrations using data sets from the UCI Machine Learning repository are given. Each chapter ends with some simple exercises for you to get hands-on experience of the concepts and R packages discussed in the chapter. The last chapters are devoted to the latest development in the field, specifically Deep Learning, which uses a class of Neural Network models that are currently at the frontier of Artificial Intelligence. The book concludes with the application of Bayesian methods on Big Data using the Hadoop and Spark frameworks. Style and approach The book first gives you a theoretical description of the Bayesian models in simple language, followed by details of its implementation in the R package. Each chapter has illustrations for the use of Bayesian model and the corresponding R package, using data sets from the UCI Machine Learning repository. Each chapter also contains sufficient exercises for you to get more hands-on practice.

Hands-On Ensemble Learning with R

A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

Author: Prabhanjan Narayanachar Tattar

Publisher: Packt Publishing Ltd

ISBN: 1788629175

Category: Computers

Page: 376

View: 5584

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Explore powerful R packages to create predictive models using ensemble methods Key Features Implement machine learning algorithms to build ensemble-efficient models Explore powerful R packages to create predictive models using ensemble methods Learn to build ensemble models on large datasets using a practical approach Book Description Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy. Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples. What you will learn Carry out an essential review of re-sampling methods, bootstrap, and jackknife Explore the key ensemble methods: bagging, random forests, and boosting Use multiple algorithms to make strong predictive models Enjoy a comprehensive treatment of boosting methods Supplement methods with statistical tests, such as ROC Walk through data structures in classification, regression, survival, and time series data Use the supplied R code to implement ensemble methods Learn stacking method to combine heterogeneous machine learning models Who this book is for This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.

Learning Predictive Analytics with R

Author: Eric Mayor

Publisher: Packt Publishing Ltd

ISBN: 1782169369

Category: Computers

Page: 332

View: 7857

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Get to grips with key data visualization and predictive analytic skills using R About This Book Acquire predictive analytic skills using various tools of R Make predictions about future events by discovering valuable information from data using R Comprehensible guidelines that focus on predictive model design with real-world data Who This Book Is For If you are a statistician, chief information officer, data scientist, ML engineer, ML practitioner, quantitative analyst, and student of machine learning, this is the book for you. You should have basic knowledge of the use of R. Readers without previous experience of programming in R will also be able to use the tools in the book. What You Will Learn Customize R by installing and loading new packages Explore the structure of data using clustering algorithms Turn unstructured text into ordered data, and acquire knowledge from the data Classify your observations using Naive Bayes, k-NN, and decision trees Reduce the dimensionality of your data using principal component analysis Discover association rules using Apriori Understand how statistical distributions can help retrieve information from data using correlations, linear regression, and multilevel regression Use PMML to deploy the models generated in R In Detail R is statistical software that is used for data analysis. There are two main types of learning from data: unsupervised learning, where the structure of data is extracted automatically; and supervised learning, where a labeled part of the data is used to learn the relationship or scores in a target attribute. As important information is often hidden in a lot of data, R helps to extract that information with its many standard and cutting-edge statistical functions. This book is packed with easy-to-follow guidelines that explain the workings of the many key data mining tools of R, which are used to discover knowledge from your data. You will learn how to perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. All chapters will guide you in acquiring the skills in a practical way. Most chapters also include a theoretical introduction that will sharpen your understanding of the subject matter and invite you to go further. The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naive Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools of R as well as lattice for visualizing patterns in data organized in groups. This book is invaluable for anyone fascinated by the data mining opportunities offered by GNU R and its packages. Style and approach This is a practical book, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this book, but that can also be applied to any other data.

Machine Learning and Data Science

An Introduction to Statistical Learning Methods with R

Author: Daniel D. Gutierrez

Publisher: Technics Publications

ISBN: 1634620984

Category: Computers

Page: 282

View: 409

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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.

Web Application Development with R using Shiny

Author: Chris Beeley

Publisher: Packt Publishing Ltd

ISBN: 178328448X

Category: Computers

Page: 110

View: 3372

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This book follows a standard tutorial-based approach which will teach you how to make a web app using R and Shiny quickly and easily.This book is for anybody who wants to produce interactive data summaries over the Web, whether you want to share them with a few colleagues or the whole world. You need no previous experience with R, Shiny, HTML, or CSS to begin using this book, although you will need at least a little previous experience with programming in a different language.

Mastering Data Analysis with R

Author: Gergely Daroczi

Publisher: Packt Publishing Ltd

ISBN: 1783982039

Category: Computers

Page: 396

View: 7982

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Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R's range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.

Big Data Analytics with R and Hadoop

Author: Vignesh Prajapati

Publisher: Packt Publishing Ltd

ISBN: 1782163298

Category: Computers

Page: 238

View: 4690

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Big Data Analytics with R and Hadoop is a tutorial style book that focuses on all the powerful big data tasks that can be achieved by integrating R and Hadoop.This book is ideal for R developers who are looking for a way to perform big data analytics with Hadoop. This book is also aimed at those who know Hadoop and want to build some intelligent applications over Big data with R packages. It would be helpful if readers have basic knowledge of R.