In performance, how you organize can be the key to growth. In the past five years, the business world has seen the birth of a new breed of company - the Exponential Organization - that has revolutionized how a company can accelerate its growth by using technology.

    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.

    His blog has garnered a devoted following of millions of readers who have come to rely on him to help them run their businesses. A lifelong rap fan, Horowitz amplifies business lessons with lyrics from his favorite songs and tells it straight about everything from firing friends to poaching competitors, from cultivating and sustaining a CEO mentality to knowing the right time to cash in.His advice is grounded in anecdotes from his own hard-earned rise—from cofounding the early cloud service provider Loudcloud to building the phenomenally successful Andreessen Horowitz venture capital firm, both with fellow tech superstar Marc Andreessen (inventor of Mosaic, the Internet's first popular Web browser). This is no polished victory lap; he analyzes issues with no easy answers through his trials, includingdemoting (or firing) a loyal friend;whether you should incorporate titles and promotions, and how to handle them;if it's OK to hire people from your friend's company;how to manage your own psychology, while the whole company is relying on you;what to do when smart people are bad employees;why Andreessen Horowitz prefers founder CEOs, and how to become one;whether you should sell your company, and how to do it.Filled with Horowitz's trademark humor and straight talk, and drawing from his personal and often humbling experiences, The Hard Thing About Hard Things is invaluable for veteran entrepreneurs as well as those aspiring to their own new ventures.

    Crossing the Chasm has become the bible for bringing cutting-edge products to progressively larger markets. This edition provides new insights into the realities of high-tech marketing, with special emphasis on the Internet. It's essential reading for anyone with a stake in the world's most exciting marketplace.

    Whether it's improving customer experiences, trying out new business models, or developing new products, even the most experienced managers often get it wrong. This is especially true in the online world, where predicting customer behavior is virtually impossible.Managers can, however, discover whether a new product, service, or business model will fail or succeed--by subjecting it to rigorous experimentation. Think about it. A pharmaceutical company would never introduce a new drug without first conducting a round of experiments based on established scientific protocols. Yet that's essentially what many companies do when they roll out new products and services.As Harvard Business School professor Stefan Thomke shows in this eye-opening and essential book, the "best guess" approach to innovation is changing fast. There are now leading companies that conduct more than ten thousand online controlled experiments annually, engaging millions of users. These organizations have discovered that an "experiment with everything" approach has a big payoff, giving them a considerable competitive advantage.How can you do this at your company? Leaders and managers need to create an "experimentation organization" that masters the science of testing and puts the discipline of experimentation at the center of the innovation process. It used to take companies years to build the infrastructure and develop the expertise to run hundreds of experiments each day. But Thomke shows how, with advances in technology, these capabilities are at the fingertips of almost any business professional. By combining the power of software and the rigor of controlled experiments, today's managers can make better decisions, create better customer experiences, and generate huge financial returns.Filled with engaging and instructive stories of leading experimentation organizations, Experimentation Works will be your guidebook to a truly new way of thinking and innovating.

    Forget short-term symptoms like Donald Trump and Brexit, it is innovation itself that explains them and that will itself shape the 21st century for good and ill. Yet innovation remains a mysterious process, poorly understood by policy makers and businessmen, hard to summon into existence to order, yet inevitable and inexorable when it does happen.Matt Ridley argues in this book that we need to change the way we think about innovation, to see it as an incremental, bottom-up, fortuitous process that happens to society as a direct result of the human habit of exchange, rather than an orderly, top-down process developing according to a plan. Innovation is crucially different from invention, because it is the turning of inventions into things of practical and affordable use to people. It speeds up in some sectors and slows down in others. It is always a collective, collaborative phenomenon, not a matter of lonely genius. It is gradual, serendipitous, recombinant, inexorable, contagious, experimental and unpredictable. It happens mainly in just a few parts of the world at any one time. It still cannot be modelled properly by economists, but it can easily be discouraged by politicians. Far from there being too much innovation, we may be on the brink of an innovation famine.Ridley derives these and other lessons, not with abstract argument, but from telling the lively stories of scores of innovations, how they started and why they succeeded or in some cases failed. He goes back millions of years and leaps forward into the near future. Some of the innovation stories he tells are about steam engines, jet engines, search engines, airships, coffee, potatoes, vaping, vaccines, cuisine, antibiotics, mosquito nets, turbines, propellers, fertiliser, zero, computers, dogs, farming, fire, genetic engineering, gene editing, container shipping, railways, cars, safety rules, wheeled suitcases, mobile phones, corrugated iron, powered flight, chlorinated water, toilets, vacuum cleaners, shale gas, the telegraph, radio, social media, block chain, the sharing economy, artificial intelligence, fake bomb detectors, phantom games consoles, fraudulent blood tests, faddish diets, hyperloop tubes, herbicides, copyright and even – a biological innovation -- life itself.

    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.

    It's great to have theory and evidenced-based research at your disposal, but what will you do when somebody asks you to make your dashboard 'cooler' by adding packed bubbles and donut charts?The expert authors have a combined 30-plus years of hands-on experience helping people in hundreds of organizations build effective visualizations. They have fought many 'best practices' battles and having endured bring an uncommon empathy to help you, the reader of this book, survive and thrive in the data visualization world.A well-designed dashboard can point out risks, opportunities, and more; but common challenges and misconceptions can make your dashboard useless at best, and misleading at worst. The Big Book of Dashboards gives you the tools, guidance, and models you need to produce great dashboards that inform, enlighten, and engage.

    In Working Backwards, these two long-serving Amazon executives reveal and codify the principles and practices that drive the success of one of the most extraordinary companies the world has ever known. With twenty-seven years of Amazon experience between them, much of it in the early aughts—a period of unmatched innovation that brought products and services including Kindle, Amazon Prime, Amazon Studios, and Amazon Web Services to life—Bryar and Carr offer unprecedented access to the Amazon way as it was refined, articulated, and proven to be repeatable, scalable, and adaptable.With keen analysis and practical steps for applying it at your own company—no matter the size—the authors illuminate how Amazon’s fourteen leadership principles inform decision-making at all levels and reveal how the company’s culture has been defined by four characteristics: customer obsession, long-term thinking, eagerness to invent, and operational excellence. Bryar and Carr explain the set of ground-level practices that ensure these are translated into action and flow through all aspects of the business.Working Backwards is a practical guidebook and a corporate narrative, filled with the authors’ in-the-room recollections of what “Being Amazonian” is like and how it has affected their personal and professional lives. They demonstrate that success on Amazon’s scale is not achieved by the genius of any single leader, but rather through commitment to and execution of a set of well-defined, rigorously-executed principles and practices—shared here for the very first time. A Macmillan Audio production from St. Martin's Press

    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

    You will learn how to systematically understand, design, and implement a new business model or analyze and renovate an old one.2) Co-created by 470 strategy practitionersBusiness Model Generation practices what it preaches. Co-authored by 470 Business Model Canvas practitioners from 45 countries, the book was financed and produced independently of the traditional publishing industry. It features a tightly-integrated, visual, lie-flat design that enables immediate hands-on use.3) Designed for doersBusiness Model Generation is for those ready to abandon outmoded thinking and embrace new, innovative models of value creation: executives, consultants, entrepreneurs and leaders of all organizations.

    These people are celebrities now. What was it like when they were just a couple friends with an idea? Founders like Steve Wozniak (Apple), Caterina Fake (Flickr), Mitch Kapor (Lotus), Max Levchin (PayPal), and Sabeer Bhatia (Hotmail) tell you in their own words about their surprising and often very funny discoveries as they learned how to build a company.Where did they get the ideas that made them rich? How did they convince investors to back them? What went wrong, and how did they recover?Nearly all technical people have thought of one day starting or working for a startup. For them, this book is the closest you can come to being a fly on the wall at a successful startup, to learn how it's done.But ultimately these interviews are required reading for anyone who wants to understand business, because startups are business reduced to its essence. The reason their founders become rich is that startups do what businesses do--create value--more intensively than almost any other part of the economy. How? What are the secrets that make successful startups so insanely productive? Read this book, and let the founders themselves tell you.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    This internationally recognized standard provides the essential tools to practice project management and deliver organizational results.

    Features chapters on finance, marketing, accounting, strategy, quantitative analysis, operations, economics, organisational behaviour, and ethics, all revised to reflect the contemporary corporate culture and economic climate.