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 NLPUse sentiment analysis to mine the emotional content of textIdentify a document's most important terms with frequency measurementsExplore relationships and connections between words with the ggraph and widyr packagesConvert back and forth between R's tidy and non-tidy text formatsUse topic modeling to classify document collections into natural groupsExamine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages

    Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps.Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.

    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.

    A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.

    The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R.About the TechnologyR is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data.About the BookR in Action is a language tutorial focused on practical problems. It presents useful statistics examples and includes elegant methods for handling messy, incomplete, and non-normal data that are difficult to analyze using traditional methods. And statistical analysis is only part of the story. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's InsidePractical data analysis, step by stepInterfacing R with other softwareUsing R to visualize dataOver 130 graphsEight reference appendixes================================Table of ContentsPart I Getting startedIntroduction to RCreating a datasetGetting started with graphsBasic data managementAdvanced data managementPart II Basic methodsBasic graphsBasic statisticsPart III Intermediate methodsRegressionAnalysis of variancePower analysisIntermediate graphsRe-sampling statistics and bootstrappingPart IV Advanced methodsGeneralized linear modelsPrincipal components and factor analysisAdvanced methods for missing dataAdvanced graphics

    In Dataclysm, Christian Rudder uses it to show us who we truly are.   For centuries, we’ve relied on polling or small-scale lab experiments to study human behavior. Today, a new approach is possible. As we live more of our lives online, researchers can finally observe us directly, in vast numbers, and without filters. Data scientists have become the new demographers.   In this daring and original book, Rudder explains how Facebook "likes" can predict, with surprising accuracy, a person’s sexual orientation and even intelligence; how attractive women receive exponentially more interview requests; and why you must have haters to be hot. He charts the rise and fall of America’s most reviled word through Google Search and examines the new dynamics of collaborative rage on Twitter. He shows how people express themselves, both privately and publicly. What is the least Asian thing you can say? Do people bathe more in Vermont or New Jersey? What do black women think about Simon & Garfunkel? (Hint: they don’t think about Simon & Garfunkel.) Rudder also traces human migration over time, showing how groups of people move from certain small towns to the same big cities across the globe. And he grapples with the challenge of maintaining privacy in a world where these explorations are possible.   Visually arresting and full of wit and insight, Dataclysm is a new way of seeing ourselves—a brilliant alchemy, in which math is made human and numbers become the narrative of our time.

    GRAPH THEORY WITH APPLICATIONS TO ENGINEERING AND COMPUTER SCIENCE-PHI-DEO, NARSINGH-1979-EDN-1

    That acclaimed book, as one reviewer put it, "made meteorologists the most unlikely heroes of recent literature." But, what if the warning system failed to provide a clear, timely notice of a major storm? Tragically, that scenario played out in Joplin, Missouri, on May 22, 2011. As a wedding, a high school graduation, and shopping trips were in progress, an invisible monster storm was developing west of the city. When it arrived, many were caught unaware. One hundred sixty-one perished and one thousand were injured. "When the Sirens Were Silent" is the gripping story of the Joplin tornado. It recounts that horrible day with a goal of insuring this does not happen again. The book gives you the tools you need to keep yourself and your family safe. Included are clever lift-out copies of the latest tornado safety rules for homes, schools, and offices.

    It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    

     They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own machine learning algorithms in Python, this book is for you.

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    Although it has a long history, the recent discovery (1992) of the body's widespread endocannabinoid system (ECS) has thrust cannabis back into the limelight again as a viable medicine. In 2012 over 2.5 million prescriptions were written for medical marijuana in the United States. CBD and THC are both cannabinoids found in cannabis. However, while THC produces a “high” in the user, CBD does not. And now legal CBD-rich hemp oil is available over-the-counter in all 50 states, without a prescription. It was discovered that the Hemp Family of plants (cannabis, marijuana) is loaded with cannabinoids that stimulate our ECS receptor sites. These sites are found in the brain, organs, glands, connective tissue and immune cells and plays regulatory roles in many physiological processes including appetite, pain-sensation, mood and memory. The primary purpose of this system revolves around maintaining balance in the body. Cannabinoids found in all varieties of cannabis work in harmony with the cannabinoids we naturally produce when our system is functioning properly. It is now coming to light that we may very well be “Endocannabinoid Deficient” and supplementing with Cannabidiol, known as CBD, may provide just what we’re missing to restore optimum health. The health benefits of cannabidiol (CBD) from natural hemp oil is this book's primary focus. It explores the similarities, differences, uses and benefits of hemp, cannabis and medical marijuana along with the interplay of THC and CBD. Their 480 other components are also discussed, such as terpenoids, flavonoids, enzymes, vitamins, etc. Make no mistake about it, the endocannabinoid system, although newly discovered, is just as important as any other bodily system, like the muscular, cardiac, circulatory or digestive system. The ECS requires its own specialized medicine as found in the Hemp Family of plants, which is also known as cannabis, and which includes all strains of marijuana. Here is a list of conditions known and/or being researched that may be helped by cannabinoid therapeutics and supplementation: Acne ADD/ADHD Addiction AIDS ALS (Lou Gehrig's Disease) Alzheimer’s Anorexia Antibiotic Resistance Anxiety Atherosclerosis Arthritis Asthma Autism Bipolar Cancer Colitis/Crohn’s Depression Diabetes Endocrine Disorders Epilepsy/Seizure Fibromyalgia Glaucoma Heart Disease Huntington’s Inflammation Irritable Bowel Kidney Disease Liver Disease Metabolic Syndrome Migraine Mood Disorders Motion Sickness Multiple Sclerosis Nausea Neurodegeneration Neuropathic Pain Obesity OCD Osteoporosis Parkinson’s Prion/Mad Cow Disease PTSD Rheumatism Schizophrenia Sickle Cell Anemia Skin Conditions Sleep Disorders Spinal Cord Injury Stress Stroke/TBI 10% of the proceeds from sales of this book will be donated to the American Cannabis Nurses Association in an effort to bring together nurses, to share, integrate and one day certify nurses in the science of endocannabinoid therapeutics in nursing practice.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    If you wouldlike to purchase both the physical text and MyProgrammingLab search for ISBN-10: 0132989999/ISBN-13: 9780132989992. That packageincludes ISBN-10: 0132855836/ISBN-13: 9780132855839 and ISBN-10: 0132891557/ISBN-13: 9780132891554. MyProgrammingLab should only be purchased when required by an instructor. In "Starting Out with Java: From Control Structures through Objects", Gaddis covers procedural programming control structures and methods before introducing object-oriented programming. As with all Gaddis texts, clear and easy-to-read code listings, concise and practical real-world examples, and an abundance of exercises appear in every chapter. "