Data Science

“Available recommendation signals cluster around Social Sciences, ificial, Intelligence, NonFiction, Artificial lists, suggesting this book may fit readers looking for creative discipline, craft, or artistic motivation. Treat this as discovery context, not a quality guarantee.”

“Available recommendation signals cluster around ificial, Intelligence, NonFiction, Artificial, Programming lists, suggesting this book may fit readers looking for big-picture nonfiction and accessible learning. Treat this as discovery context, not a quality guarantee.”

Foreword by Steven PinkerBlending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our worldprovided we ask the right questions.By the end of an average...

To really learn data science, you should not only master the toolsdata science libraries, frameworks, modules, and toolkitsbut also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.If you h...

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 pro...

Want to know how the best software engineers and architects structure their applications to make them scalable, reliable, and maintainable in the long term This book examines the key principles, algorithms, and tradeoffs of data systems, using the internals of various popular software packages and frameworks as examples.Tools at your disposal are...

New edition of the bestselling guide to deep reinforcement learning and how it's used to solve complex realworld problems. Revised and expanded to include multiagent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more Key Features Second edition of the bestselling introduction to deep reinforcement learning, ...

Your nononsense guide to making sense of machine learning Machine learning can be a mindboggling concept for the masses, but those who are in the trenches of computer Programming, know just how invaluable it is. Without machine learning, fraud detection, web search results, realtime ads on web pages, credit scoring, automation, and email spam fil...

Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this handson book, you'll build an example MLdriven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practice...

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today's Webenabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to pre...

The most comprehensive, uptodate introduction to the theory and practice of Artificial Intelligence,.The longanticipated revision of Artificial Intelligence,: A Modern Approach explores the full breadth and depth of the field of Artificial Intelligence, (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in...

“Available recommendation signals cluster around NonFiction, Big, Data, Science, Business lists, suggesting this book may fit readers looking for business judgment, leadership, or practical strategy. Treat this as discovery context, not a quality guarantee.”

Solve realworld data problems with R and machine learning. Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.5 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, handson guide by experienced machine ...

The bold futurist and bestselling author explores the limitless potential of reverseengineering the human brainRay Kurzweil is arguably today?s most influential?and often controversial?futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in humanmachine civilization?reverse engineering the b...

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in realworld data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches...

Hadoop in Practice collects nearly 100 Hadoop examples and presents them in a problem/solution format. Each technique addresses a specific task you'll face, like querying big data using Pig or writing a log file loader. You'll explore each problem step by step, learning both how to build and deploy that specific solution along with the thinking tha...

Ready to unlock the power of your data With this comprehensive guide, you'll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.You'll find illuminating case studies ...

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 thoughtprovoki...

The Web is getting faster, and the data it delivers is getting bigger. How can you handle everything efficiently This book introduces Spark, an open source cluster computing system that makes data analytics fast to run and fast to write. You?ll learn how to run programs faster, using primitives for inmemory cluster computing. With Spark, your job...

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners" Ready to crank up a virtual server and smash through petabytes of data Want to add 'Machine Learning' to your LinkedIn profileWell, hold on there...Before you embark on your epic journey into the world of machine learning, there is some theory and statistical princ...

Services like social networks, web analytics, and intelligent ecommerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does Complexity. Fortunately, scalability and simplicity are not mutually exclusive?rather than using some trendy Technology,, a different approach is needed. Big data syst...

Less than 0.5 per cent of all data is currently analysed and used. However, business leaders and managers cannot afford to be unconcerned or sceptical about data. Data is revolutionizing the way we work and it is the companies that view data as a strategic asset that will survive and thrive. Bernard Marr's Data Strategy is a musthave guide to crea...

The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades. "Machine learning," the process of automating tasks once considered the domain of highlytrained analysts and mathematicians, is the key to efficiently extr...

A revelatory exploration of the hottest trend in Technology, and the dramatic impact it will have on the economy, science, and society at large.Which paint color is most likely to tell you that a used car is in good shape How can officials identify the most dangerous New York City manholes before they explode And how did Google searches predict th...

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wideranging, interdisciplinary field that?s so clouded in hype This insightful book, based on Columbia University?s Introduction to Data Science class, tells you ...

If you?re an experienced programmer interested in crunching data, this book will get you started with machine learning?a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of handson case studi...

Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special.This book will teach you the foundations of R; three fundamental Programming, paradigms (...

The book discusses the topic of Big Data and Analytics, which is now more relevant and actual than ever before. It is written with a strong practitioner focus, not overly stressing the mathematical underpinnings but the business application instead. It consists of reallife examples from the author's personal consulting and research experience (ban...

Go ahead, be skeptical about big data. The author was?at first.When the term ?big data? first came on the scene, bestselling author Tom Davenport (Competing on Analytics, Analytics at Work) thought it was just another example of Technology, hype. But his research in the years that followed changed his mind.Now, in clear, conversational language, Dav...

“Available recommendation signals cluster around Data, Science lists, suggesting this book may fit readers looking for big-picture nonfiction and accessible learning. Treat this as discovery context, not a quality guarantee.”

Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options.This pract...

This guide is ideal if you?re a professional, manager, or student who wants practical knowledge of analyzing data, without having to get a PhD in statistics. It?s also good for people who have a PhD in statistics, but may not know how to write programs that apply statistical methods to real data.Discover how to apply the R language to data analysis...

Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognitionas well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks autom...

Data Science For Dummies 2nd Edition begins by explaining large data sets and data formats, including sample Python code for manipulating data. The book explains how to work with relational databases and unstructured data, including NoSQL. The book then moves into preparing data for analysis by cleaning it up or "munging" it. From there the book ex...

The second edition of this bestselling Python book (100,000 copies sold in print alone) uses Python 3 to teach even the technically uninclined how to write programs that do in minutes what would take hours to do by hand. There is no prior Programming, experience required and the book is loved by liberal arts majors and geeks alike.If you've ever s...

“Available recommendation signals cluster around NonFiction, Data, Science, Business, Technology lists, suggesting this book may fit readers looking for business judgment, leadership, or practical strategy. Treat this as discovery context, not a quality guarantee.”

“Available recommendation signals cluster around Data, Science lists, suggesting this book may fit readers looking for big-picture nonfiction and accessible learning. Treat this as discovery context, not a quality guarantee.”

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine ...

This practical guide provides nearly 200 selfcontained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikitlearn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection...

A stepbystep gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and Artificial Intelligence,, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work.This guide will take ...

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding job...

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 d...

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are...

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, examplebased approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum ...

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

From the back cover:Big Data has Brought about a revolution in the way we do business. Essential business decisions can today be informed by the wealth of data now at our disposal. However, gaining real value from data and business insight is a difficult task.In this accessible and stimulating guide, Sudhi Sinha provides a unique perspective on Big...