With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    You'll quickly learn the language's fundamentals, then move onto persistence, exception handling, web development, SQLite, data wrangling, and Google App Engine. You'll also learn how to write mobile apps for Android, all thanks to the power that Python gives you.We think your time is too valuable to waste struggling with new concepts. Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Python uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

    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.

    Craftsm- ship has its quiet rewards, the satisfaction that comes from building a useful object and making it work. Excitement arrives with the ?ash of insight that cracks a previously intractable problem. The spiritual quest for elegance can turn the hacker into an artist. Therearepleasuresinparsimony, insqueezingthelastdropofperformanceoutofclever algorithms and tight coding. Thegames, puzzles, andchallengesofproblemsfrominternationalprogrammingc- petitionsareagreatwaytoexperiencethesepleasureswhileimprovingyouralgorithmic and coding skills. This book contains over 100 problems that have appeared in previous programming contests, along with discussions of the theory and ideas necessary to - tack them. Instant online grading for all of these problems is available from two WWW robot judging sites. Combining this book with a judge gives an exciting new way to challenge and improve your programming skills. This book can be used for self-study, for teaching innovative courses in algorithms and programming, and in training for international competition. To the Reader Theproblemsinthisbookhavebeenselectedfromover1,000programmingproblemsat the Universidad de Valladolid online judge, available athttp: //online-judge.uva.es.The judgehasruledonwelloveronemillionsubmissionsfrom27,000registeredusersaround the world to date. We have taken only the best of the best, the most fun, exciting, and interesting problems availa

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    All chapters are supplemented by thought-provoking problems. A useful work for graduate-level students with backgrounds in computer science, operations research, and electrical engineering. "Mathematicians wishing a self-contained introduction need look no further." — American Mathematical Monthly.

    We’ve comea long way since then. With this book, Drs. Romero and Balch liftthe veil from many of these once-opaque concepts in high-techfinance. We can all benefit from learning how the cooperationbetween wetware and software creates fitter models. This bookdoes a fantastic job describing how the latest advances in financialmodeling and data science help today’s portfolio managerssolve these greater riddles. —Michael Himmel, ManagingPartner, Essex Asset ManagementI applaud Phil Romero’s willingness to write about the hedgefund world, an industry that is very private, often flamboyant,and easily misunderstood. As with every sector of the investmentlandscape, the hedge fund industry varies dramaticallyfrom quantitative “black box” technology, to fundamental researchand old-fashioned stock picking. This book helps investorsdistinguish between these diverse opposites and understandtheir place in the new evolving world of finance. —Mick Elfers,Founder and Chief Investment Strategist, Irvington Capital

    Instead, ants decide what to do based on the rate, rhythm, and pattern of individual encounters and interactions--resulting in a dynamic network that coordinates the functions of the colony. Ant Encounters provides a revealing and accessible look into ant behavior from this complex systems perspective.Focusing on the moment-to-moment behavior of ant colonies, Deborah Gordon investigates the role of interaction networks in regulating colony behavior and relations among ant colonies. She shows how ant behavior within and between colonies arises from local interactions of individuals, and how interaction networks develop as a colony grows older and larger. The more rapidly ants react to their encounters, the more sensitively the entire colony responds to changing conditions. Gordon explores whether such reactive networks help a colony to survive and reproduce, how natural selection shapes colony networks, and how these structures compare to other analogous complex systems. Ant Encounters sheds light on the organizational behavior, ecology, and evolution of these diverse and ubiquitous social insects.

    Healso discusses the social aspects of software and how free softwarecan create community and social justice.Given the current turmoil in copyright and patent laws, includingthe DMCA and proposed CBDTPA, these essays are more relevant thanever. Stallman tackles head-on the essential issues driving thecurrent changes in copyright law. He argues that for creativity toflourish, software must be free of inappropriate and overly-broadlegal constraints. Over the past twenty years his arguments andactions have changed the course of software history; this new book issure to impact the future of software and legal policies in the yearsto come.Lawrence Lessig, the author of two well-known books on similar topics,writes the introduction. He is a noted legal expert on copyright lawand a Stanford Law School professor.

    The manner in which computers are now able to mimic human thinking is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the input of data they are able to form their own decisions and take actions virtually as a human would. But as a machine, can consider many more scenarios and execute calculations to solve complex problems. This is the element that excites companies and budding machine learning engineers the most. The ability to solve complex problems never before attempted. This is also perhaps one reason why you are looking at purchasing this book, to gain a beginner's introduction to machine learning. This book provides a plain English introduction to the following topics: - Artificial Intelligence - Big Data - Downloading Free Datasets - Regression - Support Vector Machine Algorithms - Deep Learning/Neural Networks - Data Reduction - Clustering - Association Analysis - Decision Trees - Recommenders - Machine Learning Careers This book has recently been updated following feedback from readers. Version II now includes: - New Chapter: Decision Trees - Cleanup of minor errors

    With this concise book, you will learn how to use the latest version of this W3C standard to retrieve and manipulate the increasing amount of public and private data available via SPARQL endpoints. Several open source and commercial tools already support SPARQL, and this introduction gets you started right away.Begin with how to write and run simple SPARQL 1.1 queries, then dive into the language's powerful features and capabilities for manipulating the data you retrieve. Learn what you need to know to add to, update, and delete data in RDF datasets, and give web applications access to this data.Understand SPARQL’s connection with RDF, the semantic web, and related specificationsQuery and combine data from local and remote sourcesCopy, convert, and create new RDF dataLearn how datatype metadata, standardized functions, and extension functions contribute to your queriesIncorporate SPARQL queries into web-based applications

    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

    

    Historically and etymologically richer than its predecessor, it supplies additional background on existing entries and clarifies the murky origins of several important jargon terms (overturning a few long-standing folk etymologies) while still retaining its high giggle value.Sample definitionhacker n. [originally, someone who makes furniture with an axe] 1. A person who enjoys exploring the details of programmable systems and how to stretch their capabilities, as opposed to most users, who prefer to learn only the minimum necessary. 2. One who programs enthusiastically (even obsessively) or who enjoys programming rather than just theorizing about programming. 3. A person capable of appreciating {hack value}. 4. A person who is good at programming quickly. 5. An expert at a particular program, or one who frequently does work using it or on it; as in `a UNIX hacker'. (Definitions 1 through 5 are correlated, and people who fit them congregate.) 6. An expert or enthusiast of any kind. One might be an astronomy hacker, for example. 7. One who enjoys the intellectual challenge of creatively overcoming or circumventing limitations. 8. [deprecated] A malicious meddler who tries to discover sensitive information by poking around. Hence `password hacker', `network hacker'. The correct term is {cracker}.The term 'hacker' also tends to connote membership in the global community defined by the net (see {network, the} and {Internet address}). It also implies that the person described is seen to subscribe to some version of the hacker ethic (see {hacker ethic, the}).It is better to be described as a hacker by others than to describe oneself that way. Hackers consider themselves something of an elite (a meritocracy based on ability), though one to which new members are gladly welcome. There is thus a certain ego satisfaction to be had in identifying yourself as a hacker (but if you claim to be one and are not, you'll quickly be labeled {bogus}). See also {wannabee}.

    It presents the principles, paradigms, designs and implementations of modern programming languages, and contains increased coverage of the object-oriented programming paradigm. The book also covers semantics and Java.