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.

    It has made countless new businesses possible and presents a massive opportunity for large enterprises to innovate like startups and retire decades of technical debt. But making the most of the cloud requires much more from enterprises than just a technology change. Stephen Orban led Dow Jones’s journey toward digital agility as their CIO and now leads AWS’s Enterprise Strategy function, where he helps leaders from the largest companies in the world transform their businesses. As he demonstrates in this book, enterprises must re-train their people, evolve their processes, and transform their cultures as they move to the cloud. By bringing together his experiences and those of a number of business leaders, Orban shines a light on what works, what doesn’t, and how enterprises can transform themselves using the cloud.

    This book provides up-to-date information on how good project, program, and portfolio management can help you achieve organizational success. It includes over 50 samples of tools and techniques applied to one large project, and is suitable for all majors, including business, engineering, healthcare, and more.

    But with a million blogs, books, and videos out there, just where do you start? The worldwide best selling "You Don't Know JS" book series is back for a 2nd edition: "You Don't Know JS Yet". All 6 books are brand new, rewritten to cover all sides of JS for 2020 and beyond. "Get Started" prepares you for the journey ahead, first surveying the language then detailing how the rest of the You Don't Know JS Yet book series guides you to knowing JS more deeply.

    You’ll learn how to combine social web data, analysis techniques, and visualization to find what you’ve been looking for in the social haystack—as well as useful information you didn’t know existed.Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.Get a straightforward synopsis of the social web landscapeUse adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, LinkedIn, and Google+Learn how to employ easy-to-use Python tools to slice and dice the data you collectExplore social connections in microformats with the XHTML Friends NetworkApply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detectionBuild interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits"A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data." --Alex Martelli, Senior Staff Engineer, Google

    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.

    He did not crave my company, cling to and cuddle me endlessly. He showed no need to bond with me, his father, and we did not. He exhausted me, he frustrated me and he scared me. I came to dread coming home from work sometimes, or those moments when it was my turn to wrestle him into bed and begin the long struggle to settle him. I said things that will forever haunt me, like “What is wrong with that child?” and “Is he always going to be this annoying?” What I didn't know then was that he was autistic.When that realisation came, so did the beginning of my mission to understand my son, and to understand autism. This book chronicles that search for understanding and answers. It documents one parent’s attempts to come to terms with, and accept, his child. It is raw and real, sometimes confused and frightened but also, I’d like to think, written with warmth and love and an ability to smile through difficult times.This book is for anyone starting out on a pathway with their child that they did not expect. It’s also for people who, like me, are a little further down that road but still learning, still asking questions and still getting it wrong sometimes. You are not alone.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    After a year of self-study, I learned to program well enough to land a job as a software engineer II at eBay. Once I got there, I realized I was severely under-prepared. I was overwhelmed by the amount of things I needed to know but hadn't learned yet. My journey learning to program, and my experience at my first job as a software engineer were the inspiration for this book. This book is not just about learning to program; although you will learn to code. If you want to program professionally, it is not enough to learn to code; that is why, in addition to helping you learn to program, I also cover the rest of the things you need to know to program professionally that classes and books don't teach you. "The Self-taught Programmer" is a roadmap, a guide to take you from writing your first Python program, to passing your first technical interview. I divided the book into five sections: 1. Start to program in Python 3 and build your first program.2. Learn Object-oriented programming and create a powerful Python program to get you hooked.3. Learn to use tools like Git, Bash, and regular expressions. Then use your new coding skills to build a web scraper.4. Study Computer Science fundamentals like data structures and algorithms.5. Finish with best coding practices, tips for working with a team, and advice on landing a programming job.You CAN learn to program professionally. The path is there. Will you take it?

    It introduces the use of classes; shows how to write ADTs that maximize the perfomance of C++ in creating reusable code; and provides coverage of all important OO functions, including inheritance, polymorphism and encapsulation.

    It’s easy and fun with this practical, hands-on introduction. Author Scott Murray teaches you the fundamental concepts and methods of D3, a JavaScript library that lets you express data visually in a web browser. Along the way, you’ll expand your web programming skills, using tools such as HTML and JavaScript.This step-by-step guide is ideal whether you’re a designer or visual artist with no programming experience, a reporter exploring the new frontier of data journalism, or anyone who wants to visualize and share data.Learn HTML, CSS, JavaScript, and SVG basicsDynamically generate web page elements from your data—and choose visual encoding rules to style themCreate bar charts, scatter plots, pie charts, stacked bar charts, and force-directed layoutsUse smooth, animated transitions to show changes in your dataIntroduce interactivity to help users explore data through different viewsCreate customized geographic maps with dataExplore hands-on with downloadable code and over 100 examples

    Since that time, this text has been revised, tweaked, and enhanced to account for the changes found within each release of the .NET platform (1.1, 2.0, 3.0 and now 3.5)..NET 3.0 was more of an augmentative release, essentially providing three new APIs: Windows Presentation Foundation (WPF), Windows Communication Foundation (WCF) and Windows Workflow Foundation (WF). As you would expect, coverage of the "W's" has been expanded a great deal in this version of the book from the previous Special Edition text.Unlike .NET 3.0, .NET 3.5 provides dozens of C# language features and .NET APIs. This edition of the book will walk you through all of this material using the same readable approach as was found in previous editions. Rest assured, you'll find detailed coverage of Language Integrated Query (LINQ), the C# 2008 language changes (automatic properties, extension methods, anonymous types, etc.) and the numerous bells and whistles of Visual Studio 2008. What you'll learn Everything you need to knowget up to speed with C# 2008 quickly and efficiently. Discover all the new .NET 3.5 featuresLanguage Integrated Query, anonymous types, extension methods, automatic properties, and more. Get a professional footholdtargeted to appeal to experienced software professionals, this book gives you the facts you need the way you need to see them. A rock-solid foundationfocuses on everything you need to be a successful .NET 3.5 programmer, not just the new features. Get comfortable with all the core aspects of the platform including assemblies, remoting, Windows Forms, Web Forms, ADO.NET, XML web services, and much more. Who this book is forIf you're checking out this book for the first time, understand that it targets experienced software professionals and/or students of computer science (so please don't expect three chapters devoted to "for" loops). The mission of this text is to provide you with a rock-solid foundation to the C# 2008 programming language and the core aspects of the .NET platform (object-oriented programming, assemblies, file IO, Windows Forms/WPF, ASP.NET, ADO.NET, WCF, WF, etc.). Once you digest the information presented in these 33 chapters, you'll be in a perfect position to apply this knowledge to your specific programming assignments, and you'll be well equipped to explore the .NET universe on your own terms. "

    Several things make this one stand out. Here's the most important: Linux Administration Handbook is designed for administrators working in industrial-strength production environments. It never glosses over the "subtleties" that can get you in big trouble. It doesn't stint on technical detail. It's never satisfied with restating the man pages. And it's full of war stories from folks who've been there. Evi Nemeth and her coauthors: Boy, have they ever been there. (Just ask any gray-bearded Unix sysadmin about their earlier, legendary Unix System Administration Handbook.) There's only been one downside to Linux Administration Handbook: It's been nearly five years since it was written. Well, that flaw's just been remedied. The new Second Edition has been systematically revised for the latest administration tools (think Nagios and LVM). It's carefully targeted at today's five most widely used distributions: Red Hat Enterprise Linux 4.3, Fedora Core 5, SUSE Linux 10.2, Debian 3.2 "Etch," and Ubuntu 6.06. The result: a book you can rely on for the next five years. Rely on to do what? Just about everything. You'll find chapters on booting and shutting down; "rootly" powers; controlling processes; the Linux filesystem; on adding new users. You'll learn the most efficient ways to perform backups. How to make sense of syslogs and log files. Everything you need to know about drivers, the kernel, networking, NFS -- and Internet services, from web hosting to email. Nemeth & Company bring their experience to bear on troubleshooting, performance optimization, print management, security, Windows interoperability, even "policies and politics." Whatever Linux books you already own, if you depend on Linux to run efficiently and reliably, you need this one, too. Bill Camarda, from the December 2006 href="http://www.barnesandnoble.com/newslet... Only

    From Mary, Queen of Scots, trapped by her own code, to the Navajo Code Talkers who helped the Allies win World War II, to the incredible (and incredibly simple) logisitical breakthrough that made Internet commerce secure, The Code Book tells the story of the most powerful intellectual weapon ever known: secrecy.Throughout the text are clear technical and mathematical explanations, and portraits of the remarkable personalities who wrote and broke the world’s most difficult codes. Accessible, compelling, and remarkably far-reaching, this book will forever alter your view of history and what drives it. It will also make you wonder how private that e-mail you just sent really is.

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.