Financial Times Business Book of the Year Finalist“Illuminating and very timely . . . a fascinating — and sometimes alarming — survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think.”—New York TimesIt seems like “big data” is in the news every day, as we read the latest examples of how powerful algorithms are teasing out the hidden connections between seemingly unrelated things. Whether it is used by the NSA to fight terrorism or by online retailers to predict customers’ buying patterns, big data is a revolution occurring around us, in the process of forever changing economics, science, culture, and the very way we think. But it also poses new threats, from the 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. What we have already seen is just the tip of the iceberg.Big Data is the first major book about this earthshaking subject, with two leading experts explaining what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards.“An optimistic and practical look at the Big Data revolution — just the thing to get your head around the big changes already underway and the bigger changes to come.”—Cory Doctorow, boingboing.com

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. 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 advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

Business intelligence (BI) used to be so simple - in theory anyway. Integrate and copy data from your transactional systems into a specialized relational database, apply BI reporting and query tools and add business users. Job done.No longer. Analytics, big data and an array of diverse technologies have changed everything. More importantly, business is insisting on ever more value, ever faster from information and from IT in general. An emerging biz-tech ecosystem demands that business and IT work together.Business unIntelligence reflects the new reality that in today's socially complex and rapidly changing world, business decisions must be based on a combination of rational and intuitive thinking. Integrating cues from diverse information sources and tacit knowledge, decision makers create unique meaning to innovate heuristically at the speed of thought. This book provides a wealth of new models that business and IT can use together to design support systems for tomorrow's successful organizations.Dr. Barry Devlin, one of the earliest proponents of data warehousing, goes back to basics to explore how the modern trinity of information, process and people must be reinvented and restructured to deliver the value, insight and innovation required by modern businesses. From here, he develops a series of novel architectural models that provide a new foundation for holistic information use across the entire business. From discovery to analysis and from decision making to action taking, he defines a fully integrated, closed-loop business environment. Covering every aspect of business analytics, big data, collaborative working and more, this book takes over where BI ends to deliver the definitive framework for information use in the coming years.As the person who defined the conceptual framework and physical architecture for data warehousing in the 1980s, Barry Devlin has been an astute observer of the movement he initiated ever since. Now, in Business unintelligence, Devlin provides a sweeping view of the past, present, and future of business intelligence, while delivering new conceptual and physical models for how to turn information into insights and action. Reading Devlin's prose and vision of BI are comparable to reading Carl Sagan's view of the cosmos. The book is truly illuminating and inspiring.--Wayne Eckerson, President, BI Leader ConsultingAuthor, "Secrets of Analytical Leaders: Insights from Information Insiders"

Residents in Boston, Massachusetts are automatically reporting potholes and road hazards via their smartphones. Progressive Insurance tracks real-time customer driving patterns and uses that information to offer rates truly commensurate with individual safety. Google accurately predicts local flu outbreaks based upon thousands of user search queries. Amazon provides remarkably insightful, relevant, and timely product recommendations to its hundreds of millions of customers. Quantcast lets companies target precise audiences and key demographics throughout the Web. NASA runs contests via gamification site TopCoder, awarding prizes to those with the most innovative and cost-effective solutions to its problems. Explorys offers penetrating and previously unknown insights into healthcare behavior.How do these organizations and municipalities do it? Technology is certainly a big part, but in each case the answer lies deeper than that. Individuals at these organizations have realized that they don't have to be Nate Silver to reap massive benefits from today's new and emerging types of data. And each of these organizations has embraced Big Data, allowing them to make astute and otherwise impossible observations, actions, and predictions.It's time to start thinking big.In Too Big to Ignore, recognized technology expert and award-winning author Phil Simon explores an unassailably important trend: Big Data, the massive amounts, new types, and multifaceted sources of information streaming at us faster than ever. Never before have we seen data with the volume, velocity, and variety of today. Big Data is no temporary blip of fad. In fact, it is only going to intensify in the coming years, and its ramifications for the future of business are impossible to overstate.Too Big to Ignore explains why Big Data is a big deal. Simon provides commonsense, jargon-free advice for people and organizations looking to understand and leverage Big Data. Rife with case studies, examples, analysis, and quotes from real-world Big Data practitioners, the book is required reading for chief executives, company owners, industry leaders, and business professionals.

A New York Times bestseller"Brilliant, funny…the best math teacher you never had." ―San Francisco ChronicleOnce considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called "sexy." From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal―and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.

Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation.Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it.

'If you think you understand AI and all of the related issues, you don't. By the time you finish this exceptionally lucid and riveting book you will breathe more easily and wisely' - Michael Gazzaniga\\nA leading computer scientist brings human sense to the AI bubble\\nNo recent scientific enterprise has been so alluring, terrifying and filled with extravagant promise and frustrating setbacks as artificial intelligence. Writing with clarity and passion, leading AI researcher Melanie Mitchell offers a captivating account of modern-day artificial intelligence.\\nFlavoured with personal stories and a twist of humour, Artificial Intelligence illuminates the workings of machines that mimic human learning, perception, language, creativity and common sense. Weaving together advances in AI with cognitive science and philosophy, Mitchell probes the extent to which today's 'smart' machines can actually think or understand, and whether AI even requires such elusive human qualities at all.\\nArtificial Intelligence: A Guide for Thinking Humans provides readers with an accessible and clear-eyed view of the AI landscape, what the field has actually accomplished, how much further it has to go and what it means for all of our futures.

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Learn what it takes to succeed in the the most in-demand tech jobHarvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code.The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situationsDeveloping Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates.

Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."Karolis Urbonas, Head of Data Science at Amazon: "A great introduction to machine learning from a world-class practitioner."Chao Han, VP, Head of R&D at Lucidworks: "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."Sujeet Varakhedi, Head of Engineering at eBay: "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''Vincent Pollet, Head of Research at Nuance: "The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: "This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."Everything you really need to know in Machine Learning in a hundred pages.

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software.This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science CertificationGet started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

Product Description Did you know that according to Harvard Business Review the Data Scientist is the sexiest job of the 21st century? And for a reason!If "sexy" means having rare qualities that are much in demand, data scientists are already there. They are expensive to hire and, given the very competitive market for their services, difficult to retain. There simply aren't a lot of people with their combination of scientific background and computational and analytical skills.Data Science is all about transforming data into business value using math and algorithms. And needless to say, Python is the must-know programming language of the 21st century. If you are interested in coding and Data Science, then you must know Python to succeed in these industries! Data Science for Beginners is the perfect place to start learning everything you need to succeed. Contained within these four essential books are the methods, concepts, and important practical examples to help build your foundation for excelling at the discipline that is shaping the modern word. This bundle is perfect for programmers, software engineers, project managers and those who just want to keep up with technology. With these books in your hands, you will: ● Learn Python from scratch including the basic operations, how to install it, data structures and functions, and conditional loops ● Build upon the fundamentals with advanced techniques like Object-Oriented Programming (OOP), Inheritance, and Polymorphism● Discover the importance of Data Science and how to use it in real-world situations ● Learn the 5 steps of Data Analysis so you can comprehend and analyze data sitting right in front of you ● Increase your income by learning a new, valuable skill that only a select handful of people take the time to learn ● Discover how companies can improve their business through practical examples and explanations ● And Much More! This bundle is essential for anyone who wants to study Data Science and learn how the world is moving to an open-source platform. Whether you are a software engineer or a project manager, jump to the next level by developing a data-driven approach and learning how to define a data-driven vision of your business!Order Your Copy of the Bundle and Start Your New Career Path Today! Review This 4 book set focuses on Python programming. The 1st book details how easy Python is to learn and use as opposed to something C++. I liked that terms are explained in great detail and there are tons of examples. Every step has a screen grab to make sure you are following along in the learning process. Book 2 focuses on data analysis with Python. So anyone in the business world with definitely benefit from this book. The ways Python can streamline and speed up data analysis is examined. Again there are tons of examples and it is easy to follow. Book 3 focuses on Machine Learning. Python seems to be the future of AI, and one who hopes to build a future in that arena needs a strong foundation in Python. The author dispels myths and fears that are common when AI is discussed especially that it will take people's jobs. Instead the author believes AI will create just as many jobs, just in different areas. Book 4's subject is data science. And the benefits Python can bring to that area of business and math. Again, this book is loaded with examples and every formula and chart is explained so well. I was not lost at all. The author comes across as a highly knowledgeable expert imparting credible info. I recommend this to anyone wanting to explore Python more. - J. Mielke

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinctheft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well.Additional learning tools:Contains “War Stories,” offering perspectives on how data science applies in the real worldIncludes “Homework Problems,” providing a wide range of exercises and projects for self-studyProvides a complete set of lecture slides and online video lectures at www.data-manual.comProvides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapterRecommends exciting “Kaggle Challenges” from the online platform KaggleHighlights “False Starts,” revealing the subtle reasons why certain approaches failOffers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)

Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect: Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It's a big, big data world out there―let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

text is designed for an introductory probability course at the university level for sophomores, juniors, and seniors in mathematics, physical and social sciences, engineering, and computer science. It presents a thorough treatment of ideas and techniques necessary for a firm understanding of the subject. The text is also recommended for use in discrete probability courses. The material is organized so that the discrete and continuous probability discussions are presented in a separate, but parallel, manner. This organization does not emphasize an overly rigorous or formal view of probabililty and therefore offers some strong pedagogical value. Hence, the discrete discussions can sometimes serve to motivate the more abstract continuous probability discussions. Features: Key ideas are developed in a somewhat leisurely style, providing a variety of interesting applications to probability and showing some nonintuitive ideas. Over 600 exercises provide the opportunity for practicing skills and developing a sound understanding of ideas. Numerous historical comments deal with the development of discrete probability. The text includes many computer programs that illustrate the algorithms or the methods of computation for important problems.

"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer"The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.comAward-winning | Used by over 30 universities | Translated into 9 languagesAn introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques.Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections.How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics(aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more.How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more.A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

THE BEST SQL BOOK FOR BEGINNERS IN 2024 - HANDS DOWN!**Includes FREE Digital Bonuses! Sample Database, SQL Browser App, and More!**Learn Why QuickStart Guides are Loved by Over 1 Million Readers Around the WorldThe same book currently in used in college-level courses while remaining approachable for beginners! The Easiest Way to Learn SQL in a Comprehensive, Step-by-Step GuideNot sure how to prepare for the data-driven future?This book shows you EXACTLY what you need to know to successfully use the SQL programming language to enhance your career!Are you a developer who wants to expand your mastery to database management?Then you NEED this book. Buy now and start reading today!The ubiquity of big data means that now more than ever there is a burning need to warehouse, access, and understand the contents of massive databases quickly and efficiently.That’s where SQL comes in.SQL is the workhorse programming language that forms the backbone of modern data management and interpretation.Any database management professional will tell you that despite trendy data management languages that come and go, SQL remains the most widely used and most reliable to date, with no signs of stopping. Written by an SQL Expert with Over 25 Years of ExperienceIn this comprehensive guide, experienced mentor and SQL expert Walter Shields draws on his considerable knowledge to make the topic of relational database management accessible, easy to understand, and highly actionable. SQL QuickStart Guide is Perfect for: Professionals looking to augment their job skills in preparation for a data-driven future Job seekers who want to pad their skills and resume for a durable employability edge Beginners with zero prior experience Managers, decision makers, and business owners looking to manage data-driven business insights Developers looking to expand their mastery beyond the full stack Anyone who wants to be better prepared for our data-driven future!With SQL QuickStart Guide, You'll Easily Understand These Crucial Concepts: The basic structure of databases—what they are, how they work, and how to successfully navigate them How to use SQL to retrieve and understand data no matter the scale of a database (aided by numerous images and examples) The most important SQL queries, along with how and when to use them for best effect Professional applications of SQL and how to “sell” your new SQL skills to your employer, along with other career-enhancing considerationsMakes a Great Gift for a Programmer in Your Life! **LIFETIME ACCESS TO FREE BONUS SQL RESOURCES**SQL QuickStart Guide comes with lifetime access to FREE digital resources you can access from inside the book! Each of these bonuses is crafted with our expert author to help you become a better programmer including: Sample Database & Hands-on Exercises SQL Commands Cheat Sheet and more!Join thousands of other readers who have used this QuickStart Guide to learn how to manage databases - Grab your copy of SQL QuickStart Guide today!

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. 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

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.Use machine learning to understand your customers, frame decisions, and drive value Spreadsheet models and pivot tables were once the cutting-edge tools of business analysis. But the Big Data revolution has changed everything. Tasks that once required armies of business analysts are being automated and scaled with software, allowing decision makers to go deep into the data to understand how their business is running and what their customers want. This has led to a new superstar job class: the Business Data Scientist who is able to combine science and engineering tools with business and economic context to build data analyses that drive better decisions.Matt Taddy, developer of the Big Data curriculum at the University of Chicago Booth School of Business, has made a career of training students to use economic principles to connect business decisions to massive data. Business Data Science is an essential primer for those who want to use cutting-edge machine learning to have a real impact on the direction of their business. With Business Data Science, readers will learn:\nThe key ingredients that make ML work, without getting lost in the hype, and a playbook for how ML and AI can be used to solve business problems\nA wealth of real-world examples, including applications of text analysis, pricing and demand estimation, A/B experiments, and customer behavior analysis\nHow to move from correlation to causation and to use ML tools to make business decisions\nAn example-driven education in scripting in R, including a wealth of R-code examples, giving you a launch pad for your own work\nScalable frameworks ideal for modern cloud computing environments\nWith Business Data Science you have everything you need to connect business problems to data and drive decisions with data analysis. You'll understand your customers better, make more informed business decisions, achieve maximum value--and thrive in today's data-driven economy.

Are you drowning in a sea of data? Would you like to take control of your data and analysis to quickly answer your business questions and make critical decisions? Do you want to confidently present results and solutions to managers, colleagues, clients and the public?If so, you are an Accidental Analyst! Although you didn't plan for a career as a data analyst, you're now in a position where you have to analyze data to be successful. Whether you've been working with data for a few years or are just getting started, you can learn how to analyze your data to find answers to real-world questions. Even if you're an expert, you'll find creative ideas on how to work with accidental analysts. Using illustrated examples, we'll walk you through a clear, step-by-step framework that we call The Seven C's of Data Analysis.Read this book for inspiration, ideas and confidence to begin tackling the problems you face at work. Keep it by your desk as a reference on how to organize, analyze and display your data using best practices of visual analytics. Don't worry, you can continue to use your favorite spreadsheet or data analysis software—this information is not tied to any particular application. Throughout the book, we also include tips, tricks, and shortcuts that took years of analyzing data to discover and understand!This book is valuable for users of Microsoft Excel, Microsoft Access, Business Objects, Cognos, JMP, Microstrategy, Panopticon Software, QlikView, R, SAS, SPSS, Tableau Software or Tibco Spotfire.Please visit us at www.AccidentalAnalyst.com for articles, our free newsletter and upcoming events."This is a wonderful book, filled with practical advice.... a great resource for building analytical prowess."Stephen FewBest-selling author of "Show Me the Numbers" and "Now You See It""Finally, a book that clearly explains the fundamentals of business analytics!"Tim LatendressFinancial Analyst"This book is an amazing resource for regular business people."Diego SaenzPresident, Petplace and former CIO of Pepsi Latin AmericaIn his talk at the 2012 Tableau Conference, Pat Hanrahan, PhD,explained that the book changed how he thinks about analytics andinspired him to develop a new approach to teaching.Professor at Stanford UniversityCo-founder of Tableau and PixarTwo Academy AwardsEileen McDaniel, PhDCo-Founder and Managing Partner of Freakalytics, specializing in educational materials and analytical training with the goal of empowering people to get the most out of their data and take decisive action in their daily work. Her unique experience in science and business inspired her to adapt the scientific method for business, resulting in the Seven C's framework. She also is co-author of Rapid Graphs with Tableau and the Rapid Dashboards Reference Card and App.Stephen McDanielCo-Founder and Principal Data Scientist of Freakalytics. He has over 25 years of experience as a statistician, analyst, data architect, instructor, data miner and software innovator. He has been a faculty member at The Data Warehouse Institute (TDWI) and at The American Marketing Association. He is lead author of SAS for Dummies and has worked with over 100 organizations including Netflix, SAS, Tableau, UC-Berkeley, Duke and the US Navy.

Often termed as the ‘new gold,’ the vast amount of social media data can be employed to identify which customer behavior and actions create more value. Nevertheless, many brands find it extremely hard to define what the value of social media is and how to capture and create value with social media data. In Creating Value with Social Media Analytics, we draw on developments in social media analytics theories and tools to develop a comprehensive social media value creation framework that allows readers to define, align, capture, and sustain value through social media data. The book offers concepts, strategies, tools, tutorials, and case studies that brands need to align, extract, and analyze a variety of social media data, including text, actions, networks, multimedia, apps, hyperlinks, search engines, and location data. By the end of this book, the readers will have mastered the theories, concepts, strategies, techniques, and tools necessary to extract business value from big social media that help increase brand loyalty, generate leads, drive traffic, and ultimately make sound business decisions. Here is how the book is organized. Chapter 1: Creating Value with Social Media Analytics Chapter 2: Understanding Social Media Chapter 3: Understanding Social Media Analytics Chapter 4: Analytics-Business Alignment Chapter 5: Capturing Value with Network Analytics Chapter 6: Capturing Value with Text Analytics Chapter 7: Capturing Value with Actions Analytics Chapter 8: Capturing Value with Search Engine Analytics Chapter 9: Capturing Value with Location Analytics Chapter 10: Capturing Value with Hyperlinks Analytics Chapter 11: Capturing Value with Mobile Analytics Chapter 12: Capturing Value with Multimedia Analytics Chapter 13: Social Media Analytics Capabilities Chapter 14: Social Media Security, Privacy, & Ethics The book has a companion site (https://analytics-book.com/), which offers useful instructor resources. Praises for the book “Gohar F. Khan has a flair for simplifying the complexity of social media analytics. Creating Value with Social Media Analytics is a beautifully delineated roadmap to creating and capturing business value through social media. It provides the theories, tools, and creates a roadmap to leveraging social media data for business intelligence purposes. Real world analytics cases and tutorials combined with a comprehensive companion site make this an excellent textbook for both graduate and undergraduate students.” —Robin Saunders, Director of the Communications and Information Management Graduate Programs, Bay Path University. “Creating Value with Social Media Analytics offers a comprehensive framework to define, align, capture, and sustain business value through social media data. The book is theoretically grounded and practical, making it an excellent resource for social media analytics courses.” —Haya Ajjan, Director & Associate Prof., Elon Center for Organizational Analytics, Elon University. “Gohar Khan is a pioneer in the emerging domain of social media analytics. This latest text is a must-read for business leaders, managers, and academicians, as it provides a clear and concise understanding of business value creation with social media data from a social lens.” —Laeeq Khan, Director, Social Media Analytics Research Team, Ohio University. “Whether you are coming from a business, research, science or art background, Creating Value with Social Media Analytics is a brilliant induction resource for those entering the social media analytics industry. The insightful case studies and carefully crafted tutorials are the perfect supplements to help digest the key concepts introduced in each chapter.” —Jared Wong, Social Media Data Analyst, Digivizer

This book is written for high school and college students learning about probability for the first time. It will appeal to the reader who has a healthy level of enthusiasm for understanding how and why the various results of probability come about. All of the standard introductory topics in probability are covered: combinatorics, the rules of probability, Bayes’ theorem, expectation value, variance, probability density, common distributions, the law of large numbers, the central limit theorem, correlation, and regression. Calculus is not a prerequisite, although a few of the problems do involve calculus. These are marked clearly.The book features 150 worked-out problems in the form of examples in the text and solved problems at the end of each chapter. These problems, along with the discussions in the text, will be a valuable resource in any introductory probability course, either as the main text or as a helpful supplement.

Marc Andreesen once said that "markets that don't exist don't care how smart you are." Whether you're a startup founder trying to disrupt an industry, or an intrapreneur trying to provoke change from within, your biggest risk is building something nobody wants.Lean Analytics can help. By measuring and analyzing as you grow, you can validate whether a problem is real, find the right customers, and decide what to build, how to monetize it, and how to spread the word. Focusing on the One Metric That Matters to your business right now gives you the focus you need to move ahead--and the discipline to know when to change course.Written by Alistair Croll (Coradiant, CloudOps, Startupfest) and Ben Yoskovitz (Year One Labs, GoInstant), the book lays out practical, proven steps to take your startup from initial idea to product/market fit and beyond. Packed with over 30 case studies, and based on a year of interviews with over a hundred founders and investors, the book is an invaluable, practical guide for Lean Startup practitioners everywhere.

You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager.Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you'll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You'll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book.Table of Contents:PART 1 - GETTING STARTED WITH DATA SCIENCE1. What is data science?2. Data science companies3. Getting the skills4. Building a portfolioPART 2 - FINDING YOUR DATA SCIENCE JOB5. The search: Identifying the right job for you6. The application: Résumés and cover letters7. The interview: What to expect and how to handle it8. The offer: Knowing what to acceptPART 3 - SETTLING INTO DATA SCIENCE9. The first months on the job10. Making an effective analysis11. Deploying a model into production12. Working with stakeholdersPART 4 - GROWING IN YOUR DATA SCIENCE ROLE13. When your data science project fails14. Joining the data science community15. Leaving your job gracefully16. Moving up the ladder

Data Analytics: Become A Master In Data AnalyticsAnalyzing data is not easy, due to the fact that you have to figure out which type of data analytics you are going to use, as well as defeat the challenges that you will come up against when it comes to analyzing data.With this book, it is our goal to show you the easiest way to work with data analytics and how you are going to avoid some of the challenges and risks that you will be putting yourself up against when you are working with data.You will realize that analyzing data is not the easiest thing in the world. However, it is going to get easier the more that you practice. Just guarantee that you are taking the time to practice and do not put too much pressure on yourself.In this book, you are going to learn:The risks of data analytics The types of data analytics that are out there in the world What the decision tree is The benefits of using data analytics Real world examples that will show you how you are going to be able to take this knowledge and apply it to your everyday life.Data analysis happens no matter what line of work you are in, and it is my hope that with this book, you are able to learn everything that pushes you further in your knowledge of data analysis!Get Your Copy Today!

Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, youâ??ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what youâ??ve learned, and concludes with exercises, most of which involve writing R code. Write a simple R program, and discover what the language can do Use data types such as vectors, arrays, lists, data frames, and strings Execute code conditionally or repeatedly with branches and loops Apply R add-on packages, and package your own work for others Learn how to clean data you import from a variety of sources Understand data through visualization and summary statistics Use statistical models to pass quantitative judgments about data and make predictions Learn what to do when things go wrong while writing data analysis code

This old edition of Now You See It is no longer in print. Please see the new edition.

Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.

Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their dataEnterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how.Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments.When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise.By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computingWhen they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Seventh Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments.

Master the math needed to excel in data science and machine learning. If you're a data scientist who lacks a math or scientific background or a developer who wants to add data domains to your skillset, this is your book. Author Hadrien Jean provides you with a foundation in math for data science, machine learning, and deep learning. Through the course of this book, you'll learn how to use mathematical notation to understand new developments in the field, communicate with your peers, and solve problems in mathematical form. You'll also understand what's under the hood of the algorithms you're using. Learn how to: Use Python and Jupyter notebooks to plot data, represent equations, and visualize space transformations Read and write math notation to communicate ideas in data science and machine learning Perform descriptive statistics and preliminary observation on a dataset Manipulate vectors, matrices, and tensors to use machine learning and deep learning libraries such as TensorFlow or Keras Explore reasons behind a broken model and be prepared to tune and fix it Choose the right tool or algorithm for the right data problem

This textbook provides a single source introduction to the primary approaches to machine learning. It is intended for advanced undergraduate and graduate students, as well as for developers and researchers in the field. No prior background in artificial intelligence or statistics is assumed. Several key algorithms, example data sets, and project-oriented home work assignments discussed in the book are accessible through the World Wide Web.Several new chapters are available from the author's website.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Used as course material in top universities like Stanford and Cambridge.\nSold in over 85 countries and translated into more than 5 languages.\nWant to get started on data science? Our promise: no math added.\nThis book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly.\nPopular concepts covered include:\n\nA/B Testing Anomaly Detection Association Rules Clustering Decision Trees and Random Forests Regression Analysis Social Network Analysis Neural Networks\n\nFeatures:\n\nIntuitive explanations and visuals Real-world applications to illustrate each algorithm Point summaries at the end of each chapter Reference sheets comparing the pros and cons of algorithms Glossary list of commonly-used terms\n\nWith this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.

This introductory textbook provides an inexpensive, brief overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. Each chapter describes a different statistical technique, ranging from basic concepts like central tendency and describing distributions to more advanced concepts such as t tests, regression, repeated measures ANOVA, and factor analysis. Each chapter begins with a short description of the statistic and when it should be used. This is followed by a more in-depth explanation of how the statistic works. Finally, each chapter ends with an example of the statistic in use, and a sample of how the results of analyses using the statistic might be written up for publication. A glossary of statistical terms and symbols is also included. Using the author’s own data and examples from published research and the popular media, the book is a straightforward and accessible guide to statistics.New features in the fourth edition include: sets of work problems in each chapter with detailed solutions and additional problems online to help students test their understanding of the material new "Worked Examples" to walk students through how to calculate and interpret the statistics featured in each chapter new examples from the author’s own data and from published research and the popular media to help students see how statistics are applied and written about in professional publications many more examples, tables, and charts to help students visualize key concepts, clarify concepts, and demonstrate how the statistics are used in the real world a more logical flow, with correlation directly preceding regression, and a combined glossary appearing at the end of the book a Quick Guide to Statistics, Formulas, and Degrees of Freedom at the start of the book, plainly outlining each statistic and when students should use them greater emphasis on (and description of) effect size and confidence interval reporting, reflecting their growing importance in research across the social science disciplines an expanded website at www.routledge.com/cw/urdan with PowerPoint presentations, chapter summaries, a new test bank, interactive problems and detailed solutions to the text’s work problems, SPSS datasets for practice, links to useful tools and resources, and videos showing how to calculate statistics, how to calculate and interpret the appendices, and how to understand some of the more confusing tables of output produced by SPSSStatistics in Plain English, Fourth Edition is an ideal guide for statistics, research methods, and/or for courses that use statistics taught at the undergraduate or graduate level, or as a reference tool for anyone interested in refreshing their memory about key statistical concepts. The research examples are from psychology, education, and other social and behavioral sciences.

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying 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 or Python programming languages 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 science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data

Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a robust artificial intelligence that can make our lives better.\\n“Finally, a book that tells us what AI is, what AI is not, and what AI could become if only we are ambitious and creative enough.” —Garry Kasparov, former world chess champion and author of Deep Thinking\\nDespite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence.\\nThe real world, in contrast, is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Taking inspiration from the human mind, Marcus and Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won't need to worry about a future of machine overlords. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust—in our homes, our cars, and our doctors' offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better.

Wouldn't It Be Great If There Were A Statistics Book That Made Histograms, Probability Distributions, And Chi Square Analysis More Enjoyable Than Going To The Dentist? Head First Statistics Brings This Typically Dry Subject To Life, Teaching You Everything You Want And Need To Know About Statistics Through Engaging, Interactive, And Thought-provoking Material, Full Of Puzzles, Stories, Quizzes, Visual Aids, And Real-world Examples. Whether You're A Student, A Professional, Or Just Curious About Statistical Analysis, Head First's Brain-friendly Formula Helps You Get A Firm Grasp Of Statistics So You Can Understand Key Points And Actually Use Them. Learn To Present Data Visually With Charts And Plots; Discover The Difference Between Taking The Average With Mean, Median, And Mode, And Why It's Important; Learn How To Calculate Probability And Expectation; And Much More

In this "important and comprehensive" guide to statistical thinking (New Yorker), discover how data literacy is changing the world and gives you a better understanding of life's biggest problems.     The age of big data has made statistical literacy more important than ever. In The Art of Statistics, David Spiegelhalter shows how to apply statistical reasoning to real-world problems. Whether we're analyzing preventative medical screening or the terrible crime sprees of serial killers, Spiegelhalter teaches us how to clarify questions, assumptions, and expectations and, most importantly, how to interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to the power of data.   "A call to arms for greater societal data literacy . . . a reminder that there are passionate, self-aware statisticians who can argue eloquently that their discipline is needed now more than ever." -- Financial Times

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, 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 a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details

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Thelong-anticipated revision of ArtificialIntelligence: A Modern Approach explores the full breadth and depth of the field of artificialintelligence (AI). The 4th Edition brings readers up to date on the latest technologies,presents concepts in a more unified manner, and offers new or expanded coverageof machine learning, deep learning, transfer learning, multi agent systems,robotics, natural language processing, causality, probabilistic programming,privacy, fairness, and safe AI.

A great building requires a strong foundation. This book teaches basic Artificial Intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. These are not just foundational algorithms for the rest of the series, but are very useful in their own right. The book explains all algorithms using actual numeric calculations that you can perform yourself. Artificial Intelligence for Humans is a book series meant to teach AI to those without an extensive mathematical background. The reader needs only a knowledge of basic college algebra or computer programming—anything more complicated than that is thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, R, Python and C. Other languages planned.

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

Brand New

Written With The Aim Of Becoming The Primary Resource For Students Of Business Analytics, This Book Provides A Holistic Perspective Of Analytics With Theoretical Foundations And Applications Of The Theory Using Examples Across Several Industries.

Major changes in this edition include the substitution of probabilistic arguments for combinatorial artifices, and the addition of new sections on branching processes, Markov chains, and the De Moivre-Laplace theorem.