The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    The box set contains 4 FULL-LENGTH novels in Drea Stein's Queensbay small town contemporary romance series.  Travel to Queensbay, a quaint New England town where love is in the air.  Meet the couples of Queensbay as they live, laugh and find their happy ever afters. DINNER FOR TWO - BOOK 1 Sean Callahan was big shot chef with his own restaurant and a tv deal - until one mistake had him back scrubbing pots. Now he has a second shot at doing what he loves and he's not about to let anything get between him and his dreams - including the beautiful but fiery Darby Reese. Darby Reese secretly quit her big city job to take over a struggling cafe. Now she has just one chance to make it work. When these two passionate chefs meet sparks fly - but is their chemistry kitchen magic or a recipe for disaster? ROUGH HARBOR - BOOK 2 Will Caitlyn Montgomery and Noah Randall be able to give first love a second chance? Noah Randall was Caitlyn Montgomery’s first love. But he left Queensbay ten years ago and broke her heart. And then when her beloved grandfather died amid scandal, Caitlyn left too, vowing never to return. After a bitter break up and career set back in London, Caitlyn has returned to Queensbay to work for Maxwell Randall, an old family friend. So far, bit by bit, Caitlyn’s been rebuilding all she lost after her ex fiancé humiliated her …and tried to ruin her professional reputation. But her past comes back to haunt her when Maxwell unexpectedly turns up dead. Not only does Caitlyn find her career in jeopardy but her heart is too, when Noah, Maxwell’s son, returns to Queensbay. Once, Caitlyn was sure Noah was the one for her…but the tragedy of her grandfather’s suicide and Noah’s decision to leave town left her bereft…and determined never to trust him again. Over the past decade, she’s managed to do her best to forget about Noah Randall. But now she can’t help wondering, what if? What if Noah really was the one? THE IVY HOUSE - BOOK 3 Phoebe Ryan is Hollywood royalty, the grand-daughter of the infamous screen siren Savannah Ryan. All of her life, Phoebe’s been a California golden girl. But when Savannah dies and leaves her penniless, Phoebe finds her life going down faster than a sinking ship. Before she knows it, she’s been dumped, fired and evicted. All of sudden the glamour of Hollywood is starting to wear a little thin. So when Phoebe discovers Savannah has left her a run-down house on the New England coast, she eagerly hops on a plane. The rundown cottage isn't what she expected. And neither is Chase Sanders, the one man whose past is linked inextricably to her own. Can a big town girl find love with a small town guy? Or is Chase only interested in her because of her famous last name? Phoebe knows that all that glitters isn't gold, but will Chase be able to convince her that his love for her is the real thing? CHASING A CHANCE - BOOK 4 For Lynn Masters, life is pretty swell. She has a job she loves, a new apartment and good friends. So she hasn’t had a date in awhile, but despite what her hormones are telling her, she’s ok with that. Then Jackson Sanders comes to town.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian Statistics the Fun Way will change that.This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples.By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to:- How to measure your own level of uncertainty in a conclusion or belief- Calculate Bayes theorem and understand what it's useful for- Find the posterior, likelihood, and prior to check the accuracy of your conclusions- Calculate distributions to see the range of your data- Compare hypotheses and draw reliable conclusions from themNext time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.

    This report provides a brief yet comprehensive introduction to Python for engineers, researchers, and data scientists who are already familiar with another programming language.Author Jake VanderPlas, an interdisciplinary research director at the University of Washington, explains Python’s essential syntax and semantics, built-in data types and structures, function definitions, control flow statements, and more, using Python 3 syntax.You’ll explore:- Python syntax basics and running Python codeBasic semantics of Python variables, objects, and operators- Built-in simple types and data structures- Control flow statements for executing code blocks conditionally- Methods for creating and using reusable functionsIterators, list comprehensions, and generators- String manipulation and regular expressions- Python’s standard library and third-party modules- Python’s core data science tools- Recommended resources to help you learn more

    What You Will Learn Design and implement a series of server-side JavaScript patterns so you understand why and when to apply them in different use case scenarios Understand the fundamental Node.js components and use them to their full potential Untangle your modules by organizing and connecting them coherently Reuse well-known solutions to circumvent common design and coding issues Deal with asynchronous code with comfort and ease Identify and prevent common problems, programming errors, and anti-patterns In Detail Node.js is a massively popular software platform that lets you use JavaScript to easily create scalable server-side applications. It allows you to create efficient code, enabling a more sustainable way of writing software made of only one language across the full stack, along with extreme levels of reusability, pragmatism, simplicity, and collaboration. Node.js is revolutionizing the web and the way people and companies create their software.In this book, we will take you on a journey across various ideas and components, and the challenges you would commonly encounter while designing and developing software using the Node.js platform. You will also discover the "Node.js way" of dealing with design and coding decisions.The book kicks off by exploring the fundamental principles and components that define the platform. It then shows you how to master asynchronous programming and how to design elegant and reusable components using well-known patterns and techniques. The book rounds off by teaching you the various approaches to scale, distribute, and integrate your Node.js application.

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    In addition to giving a strong foundation in the language itself, Lubanovic shows how to use it for a range of applications in business, science, and the arts, drawing on the rich collection of open source packages developed by Python fans.It's impressive how many commercial and production-critical programs are written now in Python. Developed to be easy to read and maintain, it has proven a boon to anyone who wants applications that are quick to write but robust and able to remain in production for the long haul.This book focuses on the current version of Python, 3.x, while including sidebars about important differences with 2.x for readers who may have to deal with programs in that version.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    This causes real problems for everyday users who need to make sense of all the information available, and for software engineers who want to make their text-based applications more useful and user-friendly. Whether building a search engine for a corporate website, automatically organizing email, or extracting important nuggets of information from the news, dealing with unstructured text can be daunting.Taming Text is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. It explores how to automatically organize text, using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. This book gives examples illustrating each of these topics, as well as the foundations upon which they are built.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

    In this question-and-answer book on jQuery, you'll find a cookbook of ready-to-go solutions to help breathe life into your web page. Topics covered include: - Scrolling, Resizing and Animating Webpage elements - Backgrounds, Slideshows, and Crossfaders - Menus, Tabs, and Panels - Buttons, Fields, and Controls - Lists, Trees, and Tables - Frames, Windows, and Dialogs - Adding interactivity with Ajax - Using the jQuery User Interface Themeroller - Writing your own jQuery plug-ins All code used to create each solution is available for download and guaranteed to be simple, efficient and cross-browser compatible.

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your dataWonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

    Now maybe it can find her Mr. Right. Internet dating....What could possibly go wrong? WORTH THE FALL When Kasey loses her job, her apartment, and her guy in one day she just wants to start a new life and be a guy-free-zone. But, when one thing after another goes wrong, Mr. Right might be the one good thing worth taking a chance on. THE CATCHING KIND Hailey thought she was worth more than a straight flush until her agent loses her in a bet. Now she’s stuck being the fake girl-next-door beard for America’s Sexiest Athlete. Are arrogance and good looks a match for this stubborn Quirky Girl? THE PROPOSING KIND. Connor may have proposed. Or maybe not. Now it's up to Hailey and her Brew Crew girls figure it all out. Ridiculousness, shenanigans, arguments, and reconciliations ensue…because, Quirky Girl Power! But at the end of it all, is Hailey ready to marry America’s Most Eligible Athlete or is time to call a delay on that particular play?

    His job now, that of a rehabilitation counselor at the Brighter Days Ranch outside of Eagle Rock, Montana, keeps him busy and is fulfilling his desire to be of service. He’s put his past behind him. Or so he thinks until Vanessa “Van” Hastings shows up at the ranch with PTSD and memory loss after being held hostage in the Middle East. As Cole tries to put his resentment of her role in his career-ending mistake on the back burner, Van’s beauty, bravery and vulnerability make him rethink this woman’s purpose, both in his life and for her own. As Van recovers her memory, the couple realize more may be at stake than their affection for each other. Can they stay alive long enough to find out if love is possible for them?