Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models—from linear regression to random walks and far beyond—that can turn anyone into a genius. At the core of the book is Page's "many-model paradigm," which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.

    “Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena—from the price of airline tickets to the text of millions of books—into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior.In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing.www.big-data-book.com

    It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

    As a modern application developer you need to understand the emerging field of data management, both RDBMS and NoSQL. Seven Databases in Seven Weeks takes you on a tour of some of the hottest open source databases today. In the tradition of Bruce A. Tate's Seven Languages in Seven Weeks, this book goes beyond your basic tutorial to explore the essential concepts at the core each technology. Redis, Neo4J, CouchDB, MongoDB, HBase, Riak and Postgres. With each database, you'll tackle a real-world data problem that highlights the concepts and features that make it shine. You'll explore the five data models employed by these databases-relational, key/value, columnar, document and graph-and which kinds of problems are best suited to each. You'll learn how MongoDB and CouchDB are strikingly different, and discover the Dynamo heritage at the heart of Riak. Make your applications faster with Redis and more connected with Neo4J. Use MapReduce to solve Big Data problems. Build clusters of servers using scalable services like Amazon's Elastic Compute Cloud (EC2). Discover the CAP theorem and its implications for your distributed data. Understand the tradeoffs between consistency and availability, and when you can use them to your advantage. Use multiple databases in concert to create a platform that's more than the sum of its parts, or find one that meets all your needs at once.Seven Databases in Seven Weeks will take you on a deep dive into each of the databases, their strengths and weaknesses, and how to choose the ones that fit your needs.What You Need: To get the most of of this book you'll have to follow along, and that means you'll need a *nix shell (Mac OSX or Linux preferred, Windows users will need Cygwin), and Java 6 (or greater) and Ruby 1.8.7 (or greater). Each chapter will list the downloads required for that database.

    It also covers MySQL, and PostgreSQL. It contains examples which have been tested against each SQL platform, with incompatibilities or platform distinctives called out and explained.

    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.

    Now, he takes us further into the design realm and shows how we can apply time-honored design principles to presentation layouts.Throughout Presentation Zen Design, Garr shares his lessons on designing effective presentations that contain text, graphs, color, images, and video. After establishing guidelines for each of the various elements, he explains how to achieve an overall harmony and balance using the tenets of Zen simplicity. Not only will you discover how to design your slides for more professional-looking presentations, you'll learn to communicate more clearly and will accomplish the goal of making a stronger, more lasting connection with your audience.

    Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problemsEach chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer supportThe authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data miningMore advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data miningCovers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis

    Learn about the components of a BI architecture, how to choose the appropriate tools and technologies, and how to roll out a BI strategy throughout the organisation.

    It’s time we stand face-to-digital-face with the true powers and limitations of the algorithms that already automate important decisions in healthcare, transportation, crime, and commerce. Hello World is indispensable preparation for the moral quandaries of a world run by code, and with the unfailingly entertaining Hannah Fry as our guide, we’ll be discussing these issues long after the last page is turned.

    H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate data findings. This comprehensive how-to guide functions as a set of blueprints--supported by research and the author's extensive experience with clients in industries all over the world--for conveying data in an impactful way. Delivered in Evergreen's humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for making the chosen graph in Excel.

    This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to - Use the scientific method to evaluate hypotheses using controlled experiments - Define key metrics and ideally an Overall Evaluation Criterion - Test for trustworthiness of the results and alert experimenters to violated assumptions - Build a scalable platform that lowers the marginal cost of experiments close to zero - Avoid pitfalls like carryover effects and Twyman's law - Understand how statistical issues play out in practice.

    Considering this complex variety of data floating around us, sometimes the best — or even only — way to communicate is visually. This unique book presents a fascinating historical perspective on the subject, highlighting the work of the masters of the profession who have created a number of breakthroughs that have changed the way we communicate. Information Graphics has been conceived and designed not just for designers or graphics professionals, but for anyone interested in the history and practice of communicating visually. The in-depth introductory section, illustrated with over 60 images (each accompanied by an explanatory caption), features essays by Sandra Rendgen, Paolo Ciuccarelli, Richard Saul Wurman, and Simon Rogers; looking back all the way to primitive cave paintings as a means of communication, this introductory section gives readers an excellent overview of the subject. The second part of the book is entirely dedicated to contemporary works by the current most renowned professionals, presenting 200 graphics projects, with over 400 examples — each with a fact sheet and an explanation of methods and objectives — divided into chapters by the subjects Location, Time, Category, and Hierarchy.Features:200 projects and over 400 examples of contemporary information graphics from all over the world—ranging from journalism to art, government, education, business and much more Historical essays about the development of information graphics since its beginnings Exclusive poster (673 x 475 mm / 26.5 x 18.7 in) by Nigel Homes, who during his 20 years as graphics director for TIME revolutionized the way the magazine used information graphics

    Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace.In AI Superpowers, Kai-Fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power.Most experts already say that AI will have a devastating impact on blue-collar jobs. But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee’s opinion, probably not.  But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.