Master text-taming techniques and build effective text-processing applications with R About This Book Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide Gain in-depth understanding of the text mining process with lucid implementation in the R language Example-rich guide that lets you gain high-quality information from text data Who This Book Is For If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful. What You Will Learn Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process Access and manipulate data from different sources such as JSON and HTTP Process text using regular expressions Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA) Build a baseline sentence completing application Perform entity extraction and named entity recognition using R In Detail Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media. Style and approach This book takes a hands-on, example-driven approach to the text mining process with lucid implementation in R.
Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more Key FeaturesMaster machine learning, deep learning, and predictive modeling concepts in R 3.5Build intelligent end-to-end projects for finance, retail, social media, and a variety of domainsImplement smart cognitive models with helpful tips and best practicesBook Description R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations. What you will learnExplore deep neural networks and various frameworks that can be used in RDevelop a joke recommendation engine to recommend jokes that match users’ tastesCreate powerful ML models with ensembles to predict employee attritionBuild autoencoders for credit card fraud detectionWork with image recognition and convolutional neural networks Make predictions for casino slot machine using reinforcement learningImplement NLP techniques for sentiment analysis and customer segmentationWho this book is for If you’re a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this book.
Master machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languages Key FeaturesGain expertise in machine learning, deep learning and other techniquesBuild intelligent end-to-end projects for finance, social media, and a variety of domainsImplement multi-class classification, regression, and clusteringBook Description R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You'll tackle realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. You'll explore different clustering techniques to segment customers using wholesale data and use TensorFlow and Keras-R for performing advanced computations. You’ll also be introduced to reinforcement learning along with its various use cases and models. Additionally, it shows you how some of these black-box models can be diagnosed and understood. By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects. This Learning Path includes content from the following Packt products: R Machine Learning Projects by Dr. Sunil Kumar ChinnamgariMastering Machine Learning with R - Third Edition by Cory LesmeisterWhat you will learnDevelop a joke recommendation engine to recommend jokes that match users’ tastesBuild autoencoders for credit card fraud detectionWork with image recognition and convolutional neural networksMake predictions for casino slot machine using reinforcement learningImplement NLP techniques for sentiment analysis and customer segmentationProduce simple and effective data visualizations for improved insightsUse NLP to extract insights for textImplement tree-based classifiers including random forest and boosted treeWho this book is for If you are a data analyst, data scientist, or machine learning developer this is an ideal Learning Path for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this Learning Path.
Extract valuable data from your social media sites and make better business decisions using R About This Book Explore the social media APIs in R to capture data and tame it Employ the machine learning capabilities of R to gain optimal business value A hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media data Who This Book Is For If you have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, this book is for you. Those with experience in data analysis who are interested in mining social media data will find this book useful. What You Will Learn Access APIs of popular social media sites and extract data Perform sentiment analysis and identify trending topics Measure CTR performance for social media campaigns Implement exploratory data analysis and correlation analysis Build a logistic regression model to detect spam messages Construct clusters of pictures using the K-means algorithm and identify popular personalities and destinations Develop recommendation systems using Collaborative Filtering and the Apriori algorithm In Detail With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data. This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming. With this handy guide, you will be ready to embark on your journey as an independent social media analyst. Style and approach This easy-to-follow guide is packed with hands-on, step-by-step examples that will enable you to convert your real-world social media data into useful, practical information.
This book provides an introduction to the statistical software R and its application with an empirical approach in finance and economics. It is specifically targeted towards undergraduate and graduate students. It provides beginner-level introduction to R using RStudio and reproducible research examples. It will enable students to use R for data cleaning, data visualization and quantitative model building using statistical methods like linear regression, econometrics (GARCH etc), Copulas, etc. Moreover, the book demonstrates latest research methods with applications featuring linear regression, quantile regression, panel regression, econometrics, dependence modelling, etc. using a range of data sets and examples.
This book will provide an exposure to recent developments in the field of geostatistical modeling, spatial variability of soil resources, and preparation of digital soil maps using R and GIS and potential application of it in agricultural resource management. Specifically following major areas are covered in the book.
Learn the essence of data science and visualization using R in no time at all About This Book Become a pro at making stunning visualizations and dashboards quickly and without hassle For better decision making in business, apply the R programming language with the help of useful statistical techniques. From seasoned authors comes a book that offers you a plethora of fast-paced techniques to detect and analyze data patterns Who This Book Is For If you are an aspiring data scientist or analyst who has a basic understanding of data science and has basic hands-on experience in R or any other analytics tool, then R Data Science Essentials is the book for you. What You Will Learn Perform data preprocessing and basic operations on data Implement visual and non-visual implementation data exploration techniques Mine patterns from data using affinity and sequential analysis Use different clustering algorithms and visualize them Implement logistic and linear regression and find out how to evaluate and improve the performance of an algorithm Extract patterns through visualization and build a forecasting algorithm Build a recommendation engine using different collaborative filtering algorithms Make a stunning visualization and dashboard using ggplot and R shiny In Detail With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision. Style and approach This easy-to-follow guide contains hands-on examples of the concepts of data science using R.
So many nations today, large and small, are faced with compelling global and local circumstances, breaking acute crises, and lingering long-term chronic problems that demand leaders and followers to cope as best they can. However, there’s a growing suspicion in most everyone’s minds—from the higher classes to the lower classes, across races, religions, and various differences—where there is a deep feeling that something big needs to change. From real threats and tragic events like violence, crime, wars, global warming, mass extinctions to more specific problems of population densities to health concerns and economic near-collapse, people know that living in fear is not a quality way to live. India is a unique and great nation, with its tragic realities in the past and present, haunting its future. B. Maria Kumar, born and raised and having worked all his career in the streets, knows India well and knows what needs to change. He writes from great intellectual acumen, an understanding of history and mythology, and with vision for a better India. He has invited two colleagues to respond to his analysis of problems and solutions, each of them (Subba, a Nepali philosopher and poet living in Hong Kong, and Fisher, a Canadian philosopher and educator) to respond to his views. This book brings a trifold synthesis of how the nature and role of fear is critical to the shaping and destiny of India. Not enough development theories or thinking have invoked “fear” as a major construct to analyze, as a new way to interpret culture, religion, policies, plans and governance overall across the world. India seems the perfect location to start a new critical and creative consciousness that sets goals that the three authors believe are essential for India to make progress into the twenty-first century. Growing insecurity, uncertainty, mistrust, and corruption that accompany them is no way to build a nation resilient for the major challenges coming. In the face of a daunting task, the authors step-up boldly into the dimension of vision and realities facing a nation. They don’t shy away from saying what needs to be named, for only then will such honesty clear a path of fearlessness forward. This book will serve as a guide for many in India and its allies to rethink the ways they have understood the problems in India’s development.
This book addresses various aspects of civil and mechanical engineering field. We have included numerous neatly drawn figures and problems with solutions for the better understanding of the subject. The book is organized in six modules as per the syllabus of the first/second semester B.Tech. course under APJ Abdul Kalam Technological University, Kerala.
Construction technology focuses on principles, use of standards, and the steps involved in the design and construction of buildings and structures. We have included numerous neatly drawn figures for the better understanding of the subject. The book is organized in six modules as per the syllabus of the 4th semester B.Tech. in Civil Engineering course under APJ Abdul Kalam Technological University, Kerala.
Prem Kumar R. Memories of Teachers Educational Book MA . History Father - Ramachandran Nair Mother - Jalaja Situated in India - Kerala - Trivandrum Birth place - Nalli kuzhi E-mail-premkumartvpm18@gmail.com Phone No : 9747636278 The Book spirit was for the certificate of Panchayathi raji act The Book Contains , Old Seminar , Old Project Report, Expenses Acknowledgement Technical New Ideas for World Government Person Startups, etc Memories of Tech Is My Book None Book Role Given Some Teachers Both of Ideas.
Wishing to be healthy and happy are natural qualities of body and mind we are bestowed with. They are not purchasable commodities The World Health Organization (WHO) defines health as physical, mental, social and spiritual well-being. Our focus is so narrow that we are concerned only with physical health and indifferent to other domains of health as prescribed and described by the WHO. It is the mind that sets us on the path of mental, social and even spiritual well-being and has a profound influence on our physical well-being too. Meditation, I would say, is the panacea that can bestow upon us this wealth of health in all these domains defined. Barring a few conditions that need surgical or medical treatment, meditation can help prevent some diseases and check the progress of many others. According to some statistics carried out, about 10,000 and odd diseases are known to occur. Out of them hardly 500 are treatable and curable despite vast infrastructure and state of art technology in the field of medicine. The book doesn’t undermine the importance and utility of medical sciences, but it is only a reminder of our natural capabilities of mind and body to keep up health that is synonymous with the prevention of disease.
Photonics is a branch of physics which deals with photon Flows through a transphasor or any other photoconductive material. The photons Flow through the photo electric effect is used in mainly. The Light waves to used to conducting the photons. Such photon flow system is called photo electric effect. It photonics have a area of applications.
Industrial Systems and Engineering has emerged as a full-fledged profession in our country during the last five decades, offers the most rewarding career. It is a multi-disciplined approach to achieve higher productivity through optimum utilization of resources in any organization and to meet the emerging challenges of globalization of our economy. The contribution of Industrial Engineering is very well recognized and now it is being called upon to play an even more significant role. The future of Industrial Engineering is bright in every sector of our economy.
Dr. Mukta Makhija, Professor, Assistant Dean - IT, Head - Research and Innovation Cell, Department of Computer Applications, Integrated Academy of Management and Technology((INMANTEC), Ghaziabad, Uttar Pradesh, India. Dr.PM.Shanthi, Assistant Professor, Information Technology, J.J.College of Arts and Science, Bharathidasan University, Pudukkottai, Tamil Nadu, India. Dr. R. Rajesh, Assistant Professor, Head & IIC President, PG and Research Department of Computer Science, Kaamadhenu Arts and Science College, Sathyamangalam, Erode, Tamil Nadu, India. Dr.S.Ashok Kumar, Professor, Department of Cyber Security, Institute of Computer Science and Engineering, Saveetha School of Engineering (Saveetha University), Thandalam, Chennai, Tamil Nadu, India. Dr.C.Govindasamy, Associate Professor, Department of Computer Science & Engineering, Saveetha School of Engineering - SIMATS, Chennai, Tamil Nadu, India.
Management in all business and human organization activity is simply the act of getting people together to accomplish desired goals. Management comprises planning, organizing, staffing, leading or directing, and controlling an organization or effort for the purpose of accomplishing a goal. The Principles of Management are the essential, underlying factors that form the foundations of successful management. Essentials of management make the connection between theory and concepts to actual practice by showing how managers and organizations effectively apply the basic principles of management.
Operations management deals with the design and management of products, processes, services and supply chains. Operations management is the management of resources to create goods and services that can be sold to make a profit. These resources include employees, facilities, inventory and time. It is important because it allows a company to make profits if used properly.
Extract valuable data from your social media sites and make better business decisions using R About This Book Explore the social media APIs in R to capture data and tame it Employ the machine learning capabilities of R to gain optimal business value A hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media data Who This Book Is For If you have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, this book is for you. Those with experience in data analysis who are interested in mining social media data will find this book useful. What You Will Learn Access APIs of popular social media sites and extract data Perform sentiment analysis and identify trending topics Measure CTR performance for social media campaigns Implement exploratory data analysis and correlation analysis Build a logistic regression model to detect spam messages Construct clusters of pictures using the K-means algorithm and identify popular personalities and destinations Develop recommendation systems using Collaborative Filtering and the Apriori algorithm In Detail With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data. This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming. With this handy guide, you will be ready to embark on your journey as an independent social media analyst. Style and approach This easy-to-follow guide is packed with hands-on, step-by-step examples that will enable you to convert your real-world social media data into useful, practical information.
This book provides an introduction to the statistical software R and its application with an empirical approach in finance and economics. It is specifically targeted towards undergraduate and graduate students. It provides beginner-level introduction to R using RStudio and reproducible research examples. It will enable students to use R for data cleaning, data visualization and quantitative model building using statistical methods like linear regression, econometrics (GARCH etc), Copulas, etc. Moreover, the book demonstrates latest research methods with applications featuring linear regression, quantile regression, panel regression, econometrics, dependence modelling, etc. using a range of data sets and examples.
This book presents a comprehensive overview of non-ferrous metallurgy, especially its core principles and fundamental aspects, in a concise form. The book covers all basic concepts and definitions related to metal extraction, and provide succinct summaries of relevant metallurgical processes. It also covers the scientific and engineering aspects of nuclear processes and features special chapter on ultra-high-purity metals. The book employs a step-by-step approach, is written in an easy-to-understand style, and discusses significance of core concepts. As such, it not only offers a valuable guide for professionals and researchers working in the areas of metallurgy, mining, and chemical engineering, but can also be used as a core text in both graduate and professional coursework.
This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis. Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system. Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.
Master machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languages Key FeaturesGain expertise in machine learning, deep learning and other techniquesBuild intelligent end-to-end projects for finance, social media, and a variety of domainsImplement multi-class classification, regression, and clusteringBook Description R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You'll tackle realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. You'll explore different clustering techniques to segment customers using wholesale data and use TensorFlow and Keras-R for performing advanced computations. You’ll also be introduced to reinforcement learning along with its various use cases and models. Additionally, it shows you how some of these black-box models can be diagnosed and understood. By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects. This Learning Path includes content from the following Packt products: R Machine Learning Projects by Dr. Sunil Kumar ChinnamgariMastering Machine Learning with R - Third Edition by Cory LesmeisterWhat you will learnDevelop a joke recommendation engine to recommend jokes that match users’ tastesBuild autoencoders for credit card fraud detectionWork with image recognition and convolutional neural networksMake predictions for casino slot machine using reinforcement learningImplement NLP techniques for sentiment analysis and customer segmentationProduce simple and effective data visualizations for improved insightsUse NLP to extract insights for textImplement tree-based classifiers including random forest and boosted treeWho this book is for If you are a data analyst, data scientist, or machine learning developer this is an ideal Learning Path for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this Learning Path.
Negotiation is an important managerial skill. The ability to negotiate across cultures becomes even more challenging due to differences in institutional practices. This book explores how the institutional environment in India and China shapes their negotiating behaviour.
This book is intended to communicate information on novel drug delivery techniques, to direct tutors and learners regarding fundamental concepts in preventive pharmacy. The major aim to write this textbook is to provide information in articulate summarized manner to accomplish necessities of undergraduates as per PCI regulation. This volume is designed not only according to curriculum of undergraduate courses in pharmacy by PCI but also to communicate knowledge on SOCIAL AND PREVENTIVE PHARMACY for post graduate learners. We assured this book will be originated very valuable by graduates, post graduates, professors and industrial learners.
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