The task-based approach teaches readers how to combine HTML and CSS to create sharp and consistent Web pages.

    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.

    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

    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

    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.

    Mike Loukides kicked things off in June 2010 with “What is data science?” and from there we’ve pursued the various threads and themes that naturally emerged. Now, roughly a year later, we can look back over all we’ve covered and identify a number of core data areas: Data issues -- The opportunities and ambiguities of the data space are evident in discussions around privacy, the implications of data-centric industries, and the debate about the phrase “data science” itself. The application of data: products and processes – A “data product” can emerge from virtually any domain, including everything from data startups to established enterprises to media/journalism to education and research. Data science and data tools -- The tools and technologies that drive data science are of course essential to this space, but the varied techniques being applied are also key to understanding the big data arena.The business of data – Take a closer look at the actions connected to data -- the finding, organizing, and analyzing that provide organizations of all sizes with the information they need to compete.

    Completely updated to comprehensively reflect the most recent changes to the ISTQB Foundation Syllabus, the book adopts a practical, hands-on approach, covering the fundamental topics that every system and software tester should know. The authors are themselves developers of the ISTQB syllabus and are highly respected international authorities, teachers and authors within the field of software testing.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    Proceed step-by-step through five progressively more complex web applications: from a "Hello World" app all the way to a robust Newspaper app with a custom user model, complete user authentication flow, foreign key relationships, and more. Learn current best practices around class-based views, templates, urls, user authentication, testing, and deployment. The material is up-to-date with the latest versions of both Django (2.0) and Python (3.6). TABLE OF CONTENTS: * Introduction * Chapter 1: Initial Setup * Chapter 2: Hello World app * Chapter 3: Pages app * Chapter 4: Message Board app * Chapter 5: Blog app * Chapter 6: Forms * Chapter 7: User Accounts * Chapter 8: Custom User Model * Chapter 9: User Authentication * Chapter 10: Bootstrap * Chapter 11: Password Change and Reset * Chapter 12: Email * Chapter 13: Newspaper app * Chapter 14: Permissions and Authorizations * Chapter 15: Comments * Conclusion

    100% complete coverage of all objectives for exam RH302 Exam Readiness Checklist at the front of the book--you're ready for the exam when all objectives on the list are checked off Inside the Exam sections in every chapter highlight key exam topics covered Real-world exercises modeled after hands-on exam scenarios Two complete lab-based exams simulate the format, tone, topics, and difficulty of the real exam Bonus content (available for download) includes installation screen review, basic instructions for using VMware and Xen as testbeds, and paper and pencil versions of the lab exams Covers all RH302 exam topics, including: Hardware installation and configuration The boot process Linux filesystem administration Package management and Kickstart User and group administration System administration tools Kernel services and configuration Apache and Squid Network file sharing services (NFS, FTP, and Samba) Domain Name System (DNS) E-mail (servers and clients) Extended Internet Services Daemon (xinetd), the Secure package, and DHCP The X Window System Firewalls, SELinux, and troubleshooting

    We start off easy, defining some of the tech lingo you may have heard before, but didn’t fully understand. From there, each lesson introduces something new, slowly building up to the point where you are confident about what an API is and, for the brave, could actually take a stab at using one.

    Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner. The book covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others himself.

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    It is a great book, not just for beginning programmers, but also for computer users who would want to have an idea what is happening behind the scenes as they work with various computer programs. In this book, you are going to learn what the C programming language entails, how to write conditions, expressions, statements and even commands, for the language to perform its functions efficiently. You will learn too how to organize relevant expressions so that after compilation and execution, the computer returns useful results and not error messages. Additionally, this book details the data types that you need for the C language and how to present it as well. Simply put, this is a book for programmers, learners taking other computer courses, and other computer users who would like to be versed with the workings of the most popular computer language, C. In this book You'll learn: What Is The C Language? Setting Up Your Local Environment The C Structure and Data Type C Constants and Literals C Storage Classes Making Decisions In C The Role Of Loops In C Programming Functions in C Programming Structures and Union in C Bit Fields and Typedef Within C. C Header Files and Type Casting Benefits Of Using The C Language ...and much more!! Download your copy today! click the BUY button and download it right now!