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

    You'll find complete coverage of all exam objectives, including key topics such as project planning, execution, delivery, closure, and others. CompTIA's Project+ is the foundation-level professional exam in the complex world of project management; certified project managers often choose to go on and obtain their Project Management Professional (PMP) certifications as well Provides complete coverage of all exam objectives for CompTIA's first update to the Project+ exam in six years Covers project planning, execution, delivery, change, control, communication, and closure Demonstrates and reinforces exam preparation with practical examples and real-word scenarios Includes a CD with Sybex test engine, practice exams, electronic flashcards, and a PDF of the book Approach the new Project+ exam with confidence with this in-depth study guide! Reviews

    Included are 175 easy-to-use strategies, lessons, and checklists for effective classroom management, and over 50 reproducible samples that you can adopt immediately for planning, evaluation, or assignments. The Guide helps you create a classroom that reflects the excitement for learning that every English teacher desires.

    Aimed at undergraduate/MBA-level courses in business ethics in the departments of philosophy or business, this comprehensive text provides a balanced and up-to-date treatment of some of the most prominent issues of business ethics.

    It givessyntax and output for accomplishing many analyses through the mostrecent releases of SAS, SPSS, and SYSTAT, some not available insoftware manuals. The book maintains its practical approach, stillfocusing on the benefits and limitations of applications of a techniqueto a data set -- when, why, and how to do it. Overall, it providesadvanced students with a timely and comprehensive introduction totoday's most commonly encountered statistical and multivariatetechniques, while assuming only a limited knowledge of higher-levelmathematics.

    In 2007, Janet Belsky's "Experiencing the Lifespan" was published to widespread instructor and student acclaim, ultimately winning the 2008 Textbook Excellence Award from the Text and Academic Authors Association. Now that breakthrough text returns in a rigorously updated edition that explores the lifespan by combining the latest research with a practicing psychologist's understanding of people, and a teacher's understanding of students and classroom dynamics. And again, all of this in the right number of pages to fit comfortably in a single term course.

    There is a major emphasis on study design, with separate chapt

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    In an instructive and conversational tone, Cody clearly explains how to program SAS, illustrating with one or more real-life examples and giving a detailed description of how the program works.

    This text provides the theory you need with the practice you want. With its exciting integration of the Harley-Davidson company theme, this text continues to provide a solid, enduring foundation of the tools of modern theory in practice while at the same time developing the logic behind their use. This text uses 10 Principles of Finance as a unifying framework to tie the major concepts of the book together.

    The groups selected are peoples whose traditional cultures are uniquely their own. Each has distinctive patterns and practices; each has faced the challenge of an encroaching world, with differing results. Moreover, they often provide the prime illustrations of important concepts in introductory anthropology course including Azande witchcraft, Ju/'hoansi egalitarianism, Trobriand kula exchange, and Minangkabau matriliny. As such, this volume can stand alone as an introduction to central ethnographic concepts through these 15 societies, or serve as a valuable companion to anthropology texts. Many of the peoples presented are involved in the diaspora; some struggle to preserve old ways in new places. All sketches follow a logical, consistent organization that makes it easy for students to understand major themes such as history, subsistence, sociopolitical organization, belief systems, marriage, kinship, and contemporary issues.

    Visually engaging, enticing and current examples with an overall focus on business.Legal Environment of Business and E-Commerce; Torts, Crimes, and Intellectual Property; Contracts and E-Commerce; Domestic and International Sales and Lease Contracts; Negotiable Instruments and E-Money; Credit, Secured Transactions, and Bankruptcy; Agency and Employment; Business Organizations and Ethics; Government Regulation; Property; Special Topics; Global EnvironmentMARKET Business Law continues its dedication to being the most engaging text for readers by featuring a visually appealing format with enticing and current examples while maintaining its focus on business.

    Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

    Rothman's acclaimed Modern Epidemiology reflects the remarkable conceptual development of this evolving science and the engagement of epidemiologists with an increasing range of current public health concerns. This landmark work is the most comprehensive and cohesive text on the principles and methods of contemporary epidemiologic research.Coauthored by two leading epidemiologists, with 15 additional contributors, the Second Edition presents a much broader range of concepts and methods than Dr. Rothman's single-authored original edition. Coverage of basic measures and study types is more thorough and includes a new chapter on field methods. New chapters on advanced topics in data analysis, such as hierarchical regression, are also included. A new section covers specific areas of research such as infectious disease epidemiology, ecologic studies, disease surveillance, analysis of vital statistics, screening, clinical epidemiology, environmental and occupational epidemiology, reproductive and perinatal epidemiology, genetic epidemiology, and nutritional epidemiology.

    At a summer tea party in Cambridge, England, a guest states that tea poured into milk tastes different from milk poured into tea. Her notion is shouted down by the scientific minds of the group. But one man, Ronald Fisher, proposes to scientifically test the hypothesis. There is no better person to conduct such an experiment, for Fisher is a pioneer in the field of statistics.The Lady Tasting Tea spotlights not only Fisher's theories but also the revolutionary ideas of dozens of men and women which affect our modern everyday lives. Writing with verve and wit, David Salsburg traces breakthroughs ranging from the rise and fall of Karl Pearson's theories to the methods of quality control that rebuilt postwar Japan's economy, including a pivotal early study on the capacity of a small beer cask at the Guinness brewing factory. Brimming with intriguing tidbits and colorful characters, The Lady Tasting Tea salutes the spirit of those who dared to look at the world in a new way.