Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    In this fascinating, provocative new book, Kevin Kelly provides an optimistic road map for the future, showing how the coming changes in our lives—from virtual reality in the home to an on-demand economy to artificial intelligence embedded in everything we manufacture—can be understood as the result of a few long-term, accelerating forces. Kelly both describes these deep trends—flowing, screening, accessing, sharing, filtering, remixing, tracking, and questioning—and demonstrates how they overlap and are codependent on one another. These larger forces will completely revolutionize the way we buy, work, learn, and communicate with each other. By understanding and embracing them, says Kelly, it will be easier for us to remain on top of the coming wave of changes and to arrange our day-to-day relationships with technology in ways that bring forth maximum benefits. Kelly’s bright, hopeful book will be indispensable to anyone who seeks guidance on where their business, industry, or life is heading—what to invent, where to work, in what to invest, how to better reach customers, and what to begin to put into place—as this new world emerges.

    Though no large financial institution has recently gone bust in this country, white-collar criminals, scam artists, Ponzi schemers and organized crime, from the Hells Angels to the Russian mafia, know that Canada is the place in the Western world to rip off investors. And the fraudsters do so with little fear of being caught and punished. Thieves of Bay Street investigates Canada's biggest financial scandals of recent years. Readers will learn what banks do with investors' money and what happens when they lose it. They will meet the bogus investment gurus, the brokers who lose money with both reckless abandon and impunity, the bankers who squander money in toxic investments, the lawyers who protect them and the regulators who do nothing to keep them from doing it again. And most importantly, they'll meet the victims who are demanding that our vaunted banking sector finally come clean on its dirtiest secret.

    We are bombarded with more information each day than our brains can process—especially in election season. It's raining bad data, half-truths, and even outright lies. New York Times bestselling author Daniel J. Levitin shows how to recognize misleading announcements, statistics, graphs, and written reports revealing the ways lying weasels can use them. It's becoming harder to separate the wheat from the digital chaff. How do we distinguish misinformation, pseudo-facts, distortions, and outright lies from reliable information? Levitin groups his field guide into two categories—statistical infomation and faulty arguments—ultimately showing how science is the bedrock of critical thinking. Infoliteracy means understanding that there are hierarchies of source quality and bias that variously distort our information feeds via every media channel, including social media. We may expect newspapers, bloggers, the government, and Wikipedia to be factually and logically correct, but they so often aren't. We need to think critically about the words and numbers we encounter if we want to be successful at work, at play, and in making the most of our lives. This means checking the plausibility and reasoning—not passively accepting information, repeating it, and making decisions based on it. Readers learn to avoid the extremes of passive gullibility and cynical rejection. Levitin's charming, entertaining, accessible guide can help anyone wake up to a whole lot of things that aren't so. And catch some lying weasels in their tracks!

    With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.

    Weather can be deadly — especially when it strikes without warning. Millions of Americans could soon find themselves at the mercy of violent weather if the public data behind lifesaving storm alerts gets privatized for personal gain.In his first Audible Original feature, New York Times best-selling author and journalist Michael Lewis delivers hard-hitting research on not-so-random weather data — and how Washington plans to release it. He also digs deep into the lives of two scientists who revolutionized climate predictions, bringing warning systems to previously unimaginable levels of accuracy. One is Kathy Sullivan, a gifted scientist among the first women in space; the other, D.J. Patil, is a trickster-turned-mathematician and a political adviser.Most urgently, Lewis's narrative reveals the potential cost of putting a price tag on information with the potential to save lives, raising questions about balancing public service with profits in an ethically-ambiguous atmosphere.

    This groundbreaking new book sets out the principles and technical practices that enable rapid, incremental delivery of high quality, valuable new functionality to users. Through automation of the build, deployment, and testing process, and improved collaboration between developers, testers, and operations, delivery teams can get changes released in a matter of hours-- sometimes even minutes-no matter what the size of a project or the complexity of its code base. Jez Humble and David Farley begin by presenting the foundations of a rapid, reliable, low-risk delivery process. Next, they introduce the "deployment pipeline," an automated process for managing all changes, from check-in to release. Finally, they discuss the "ecosystem" needed to support continuous delivery, from infrastructure, data and configuration management to governance. The authors introduce state-of-the-art techniques, including automated infrastructure management and data migration, and the use of virtualization. For each, they review key issues, identify best practices, and demonstrate how to mitigate risks. Coverage includes - Automating all facets of building, integrating, testing, and deploying software - Implementing deployment pipelines at team and organizational levels - Improving collaboration between developers, testers, and operations - Developing features incrementally on large and distributed teams - Implementing an effective configuration management strategy - Automating acceptance testing, from analysis to implementation - Testing capacity and other non-functional requirements - Implementing continuous deployment and zero-downtime releases - Managing infrastructure, data, components and dependencies - Navigating risk management, compliance, and auditing Whether you're a developer, systems administrator, tester, or manager, this book will help your organization move from idea to release faster than ever--so you can deliver value to your business rapidly and reliably.

    Included are step-by-step instructions on how to carry out Bayesian data analyses.Download Link : readbux.com/download?i=0124058884            0124058884 Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan PDF by John Kruschke

    Our inaugural effort is OpenIntro Statistics. Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro.org, and anybody can purchase a paperback copy from amazon.com for under $10.The future for OpenIntro depends on the involvement and enthusiasm of our community. Visit our website, openintro.org. We provide free course management tools, including an online question bank, utilities for creating course quizzes, and many other helpful resources.CERTAIN CONTENT THAT APPEARS ON THIS SITE COMES FROM AMAZON SERVICES LLC. THIS CONTENT IS PROVIDED ‘AS IS’ AND IS SUBJECT TO CHANGE OR REMOVAL AT ANY TIME.Can’t find it here? Search Amazon.com Search: All Products Apparel & AccessoriesBabyBeautyBooksCamera & PhotoCell Phones & ServiceClassical MusicComputersComputer & Video GamesDVDElectronicsGourmet FoodHome & GardenMiscellaneousHealth & Personal CareJewelry & WatchesKitchen & HousewaresMagazine SubscriptionsMusicMusical InstrumentsSoftwareSports & OutdoorsTools & HardwareToys & GamesVHS Keywords:

    Now, in one of the greatest undertakings of the twenty-first century, a savvy group of mathematicians and computer scientists is beginning to sift through this data to dissect us and map out our next steps. Their goal? To manipulate our behavior -- what we buy, how we vote -- without our even realizing it.In this tour de force of original reporting and analysis, journalist Stephen Baker provides us with a fascinating guide to the world we're all entering -- and to the people controlling that world. The Numerati have infiltrated every realm of human affairs, profiling us as workers, shoppers, patients, voters, potential terrorists -- and lovers. The implications are vast. Our privacy evaporates. Our bosses can monitor and measure our every move (then reward or punish us). Politicians can find the swing voters among us, by plunking us all into new political groupings with names like "Hearth Keepers" and "Crossing Guards." It can sound scary. But the Numerati can also work on our behalf, diagnosing an illness before we're aware of the symptoms, or even helping us find our soul mate. Surprising, enlightening, and deeply relevant, The Numerati shows how a powerful new endeavor -- the mathematical modeling of humanity -- will transform every aspect of our lives. STEPHEN BAKER has written for BusinessWeek for over twenty years, covering Mexico and Latin America, the Rust Belt, European technology, and a host of other topics, including blogs, math, and nanotechnology. But he's always considered himself a foreign correspondent. This, he says, was especially useful as he met the Numerati. "While I came from the world of words, they inhabited the symbolic realms of math and computer science. This was foreign to me. My reporting became an anthropological mission." Baker has written for many publications, including the Wall Street Journal, the Los Angeles Times, and the Boston Globe. He won an Overseas Press Club Award for his portrait of the rising Mexican auto industry. He is the coauthor of blogspotting.net, featured by the New York Times as one of fifty blogs to watch.

    Cracking the PM Interview is a comprehensive book about landing a product management role in a startup or bigger tech company. Learn how the ambiguously-named "PM" (product manager / program manager) role varies across companies, what experience you need, how to make your existing experience translate, what a great PM resume and cover letter look like, and finally, how to master the interview: estimation questions, behavioral questions, case questions, product questions, technical questions, and the super important "pitch."

    Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business dataUpdates that incorporate input from readers, changes in the field, and more material on statistics and machine learningDozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projectsComplete classroom support for instructors at www.mkp.com/datamining2e companion site

    Over years of groundbreaking experiments, he has distilled remarkable discoveries significant enough to become the bedrock of a whole new scientific field: social physics. Humans have more in common with bees than we like to admit: We’re social creatures first and foremost. Our most important habits of action—and most basic notions of common sense—are wired into us through our coordination in social groups. Social physics is about idea flow, the way human social networks spread ideas and transform those ideas into behaviors. Thanks to the millions of digital bread crumbs people leave behind via smartphones, GPS devices, and the Internet, the amount of new information we have about human activity is truly profound. Until now, sociologists have depended on limited data sets and surveys that tell us how people say they think and behave, rather than what they actually do. As a result, we’ve been stuck with the same stale social structures—classes, markets—and a focus on individual actors, data snapshots, and steady states. Pentland shows that, in fact, humans respond much more powerfully to social incentives that involve rewarding others and strengthening the ties that bind than incentives that involve only their own economic self-interest. Pentland and his teams have found that they can study patterns of information exchange in a social network without any knowledge of the actual content of the information and predict with stunning accuracy how productive and effective that network is, whether it’s a business or an entire city. We can maximize a group’s collective intelligence to improve performance and use social incentives to create new organizations and guide them through disruptive change in a way that maximizes the good. At every level of interaction, from small groups to large cities, social networks can be tuned to increase exploration and engagement, thus vastly improving idea flow.  Social Physics will change the way we think about how we learn and how our social groups work—and can be made to work better, at every level of society. Pentland leads readers to the edge of the most important revolution in the study of social behavior in a generation, an entirely new way to look at life itself.

    mean, margin of error, standard deviation, permutations, and correlations-all using Excel

    In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect.Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor.In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile.The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values.This deeply researched and passionate book could not be more timely.Naomi Klein: "This book is downright scary."Ethan Zuckerman, MIT: "Should be required reading."Dorothy Roberts, author of Killing the Black Body: "A must-read for everyone concerned about modern tools of inequality in America."Astra Taylor, author of The People's Platform: "This is the single most important book about technology you will read this year."