It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (or MOOC) offered by the pioneering MIT--Harvard collaboration edX.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.

    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.

    This book offers concrete examples and exercises in the dynamic and versatile Python language to demonstrate and reinforce these concepts. Learn the basic tools to start writing the programs that interest you, and get a better understanding of what software can (and cannot) do. When you're finished, you'll have the necessary foundation to learn any programming language or tackle any software project you choose. With a focus on programming concepts, this book teaches you how to:Understand the core features of all programming languages, including: variables, statements, decisions, loops, expressions, and operatorsReuse code with functionsUse library code to save time and effortSelect the best data structure to manage complex dataWrite programs that talk to the WebShare your data with other programsWrite programs that test themselves and help you avoid embarrassing coding errors.Contents Intro xxiii1 Starting to Code: Finding Your Way2 Textual Data: Every String Has Its Place3 Functions: Let’s Get Organized4 Data Files and Arrays: Sort It Out5 Hashes and Databases: Putting Data in Its Place6 Modular Programming: Keeping Things Straight7 Building a Graphical User Interface: Going All Gooey8 GUIs and Data: Data Entry Widgets8 1/2 Exceptions and Message Boxes: Get the Message? 9 Graphical Interface Elements: Selecting the Right Tool10 Custom Widgets and Classes: With an Object in MindAppendix i Leftovers: The Top Ten Things (We Didn’t Cover)

    It runs on PCs rather than servers and is ideal for small- to mid-sized businesses and households. But Access is still intimidating to learn. It doesn't help that each new version crammed in yet another set of features; so many, in fact, that even the pros don't know where to find them all. Access 2007 breaks this pattern with some of the most dramatic changes users have seen since Office 95. Most obvious is the thoroughly redesigned user interface, with its tabbed toolbar (or "Ribbon") that makes features easy to locate and use. The features list also includes several long-awaited changes. One thing that hasn't improved is Microsoft's documentation. To learn the ins and outs of all the features in Access 2007, Microsoft merely offers online help.Access 2007: The Missing Manual was written from the ground up for this redesigned application. You will learn how to design complete databases, maintain them, search for valuable nuggets of information, and build attractive forms for quick-and-easy data entry. You'll even delve into the black art of Access programming (including macros and Visual Basic), and pick up valuable tricks and techniques to automate common tasks -- even if you've never touched a line of code before. You will also learn all about the new prebuilt databases you can customize to fit your needs, and how the new complex data feature will simplify your life. With plenty of downloadable examples, this objective and witty book will turn an Access neophyte into a true master.

    You’ll learn the fundamentals step-by-step with the help of numerous examples, discovering first-hand how to match, extract, and transform text by matching specific words, characters, and patterns.Regular expressions are an essential part of a programmer’s toolkit, available in various Unix utlilities as well as programming languages such as Perl, Java, JavaScript, and C#. When you’ve finished this book, you’ll be familiar with the most commonly used syntax in regular expressions, and you’ll understand how using them will save you considerable time.Discover what regular expressions are and how they workLearn many of the differences between regular expressions used with command-line tools and in various programming languagesApply simple methods for finding patterns in text, including digits, letters, Unicode characters, and string literalsLearn how to use zero-width assertions and lookaroundsWork with groups, backreferences, character classes, and quantifiersUse regular expressions to mark up plain text with HTML5

    It will help you to master various Web technologies, other than HTML5, including CSS3, JavaScript, XML, and AJAX. If you are a Web designer or developer, then this book is your introduction to new features and elements of HTML5, including audio and video media elements, the canvas element for drawing, and many others. In this book, you will learn how to use HTML5 and other Web technologies in the latest versions of modern browsers to develop Web applications.

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    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.

    In the second edition of Network Security, this most distinguished of author teams draws on hard-won experience to explain every facet of information security, from the basics to advanced cryptography and authentication; secure Web and email services; and emerging security standards. Highlights of the book's extensive new coverage include Advanced Encryption Standard (AES), IPsec, SSL, PKI Standards, and Web security.

    It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

    This Study Guide was developed to meet the exacting requirements of today's Cisco certification candidates. In addition to the engaging and accessible instructional approach that has earned author Todd Lammle the "Best Study Guide Author" award in CertCities Readers' Choice Awards for two consecutive years, this updated fifth edition provides:In-depth coverage of every CCNA exam objective Expanded IP addressing and subnetting coverage More detailed information on EIGRP and OSPF Leading-edge exam preparation software Authoritative coverage of all exam objectives, including:Network planning & designing Implementation & operation LAN and WAN troubleshooting Communications technology

    Master the fundamentals of C++ and object-oriented programming Understand how C++11 features help you write compact and efficient code using concepts such as lambda expressions, move constructors, and assignment operators Learn the Standard Template Library, including containers and algorithms used in most real-world C++ applications Test your knowledge and expertise using exercises at the end of every lesson Learn on your own time, at your own pace: No previous programming experience required Learn C++11, object-oriented programming, and analysis Write fast and powerful C++ programs, compile the source code with a gcc compiler, and create executable files Use the Standard Template Library's (STL) algorithms and containers to write feature-rich yet stable C++ applications Develop sophisticated programming techniques using lambda expressions, smart pointers, and move constructors Learn to expand your program's power with inheritance and polymorphism Master the features of C++ by learning from programming experts Learn C++11 features that allow you to program compact and high-performance C++ applications TABLE OF CONTENTSPART I: THE BASICS LESSON 1: Getting Started with C++11 LESSON 2: The Anatomy of a C++ Program LESSON 3: Using Variables, Declaring Constants LESSON 4: Managing Arrays and Strings LESSON 5: Working with Expressions, Statements, and Operators LESSON 6: Controlling Program Flow LESSON 7: Organizing Code with Functions LESSON 8: Pointers and References Explained PART II: FUNDAMENTALS OF OBJECT-ORIENTED C++ PROGRAMMING LESSON 9: Classes and Objects LESSON 10: Implementing Inheritance LESSON 11: Polymorphism LESSON 12: Operator Types and Operator Overloading LESSON 13: Casting Operators LESSON 14: An Introduction to Macros and Templates PART III: LEARNING THE STANDARD TEMPLATE LIBRARY (STL) LESSON 15: An Introduction to the Standard Template LibraryLESSON 16: The STL String ClassLESSON 17: STL Dynamic Array ClassesLESSON 18: STL list and forward_listLESSON 19: STL Set ClassesLESSON 20: STL Map ClassesPART IV: MORE STL LESSON 21: Understanding Function ObjectsLESSON 22: C++11 Lambda ExpressionsLESSON 23: STL AlgorithmsLESSON 24: Adaptive Containers: Stack and QueueLESSON 25: Working with Bit Flags Using STLPART V: ADVANCED C++ CONCEPTS LESSON 26: Understanding Smart PointersLESSON 27: Using Streams for Input and OutputLESSON 28: Exception HandlingLESSON 29: Going Forward APPENDIXES A: Working with Numbers: Binary and Hexadecimal B: C++ Keywords C: Operator Precedence D: Answers E: ASCII Codes

    Essentially, you are estimating a probability, but then updating that estimate based on other things that you know. This book is designed to give you an intuitive understanding of how to use Bayes Theorem. It starts with the definition of what Bayes Theorem is, but the focus of the book is on providing examples that you can follow and duplicate. Most of the examples are calculated in Excel, which is useful for updating probability if you have dozens or hundreds of data points to roll in.

    It is a book I recommend." --Scott Guthrie, Corporate Vice President, .NET Developer Platform, Microsoft Corporation "A must-read for a concise but thorough examination of the parallel programming features in the .NET Framework 4." --Stephen Toub, Parallel Computing Platform Program Manager, Microsoft "This wonderful book is a great reference for developers of all levels." -- Chris Burrows, C# Compiler Team, Microsoft When you have questions about how to use C# 4.0 or the .NET CLR, this highly acclaimed bestseller has precisely the answers you need. Uniquely organized around concepts and use cases, this fourth edition includes in-depth coverage of new C# topics such as parallel programming, code contracts, dynamic programming, security, and COM interoperability. You'll also find updated information on LINQ, including examples that work with both LINQ to SQL and Entity Framework. This book has all the essential details to keep you on track with C# 4.0. Get up to speed on C# language basics, including syntax, types, and variables Explore advanced topics such as unsafe code and preprocessor directives Learn C# 4.0 features such as dynamic binding, type parameter variance, and optional and named parameters Work with .NET 4's rich set of features for parallel programming, code contracts, and the code security model Learn .NET topics, including XML, collections, I/O and networking, memory management, reflection, attributes, security, and native interoperability

    according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog "AI Weirdness." She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives.We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really, and how does it solve problems, understand humans, and even drive self-driving cars?Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't--like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"?In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity. You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking.