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Designing Data-Intensive Applications: The Big Ideas Behind Relia...
by Martin Kleppmann

Language

English

Pages

624

Publication Date

March 16, 2017

Product Description
Customer Reviews
<div><p>Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?</p><p>In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.</p><ul><li>Peer under the hood of the systems you already use, and learn how to use and operate them more effectively</li><li>Make informed decisions by identifying the strengths and weaknesses of different tools</li><li>Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity</li><li>Understand the distributed systems research upon which modern databases are built</li><li>Peek behind the scenes of major online services, and learn from their architectures</li></ul></div>
The Hundred-Page Machine Learning Book
by Andriy Burkov

Language

English

Pages

Publication Date

January 12, 2019

Product Description
Customer Reviews
<b>WARNING: will not work on e-ink Kindle devices!</b><br /><br /><b>Peter Norvig</b>, Research Director at Google, co-author of <b>AIMA</b>, the most popular AI textbook in the world: <i>"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."</i><br /><br /><b>Aurélien Géron</b>, Senior AI Engineer, author of the bestseller <b>Hands-On Machine Learning with Scikit-Learn and TensorFlow</b>: <i>"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."</i><br /><br /><b>Karolis Urbonas</b>, Head of Data Science at <b>Amazon</b>: <i>"A great introduction to machine learning from a world-class practitioner."</i> <br /><br /><b>Chao Han</b>, VP, Head of R&D at <b>Lucidworks</b>: <i>"I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."</i><br /><br /><b>Sujeet Varakhedi</b>, Head of Engineering at <b>eBay</b>: <i>"Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''</i><br /><br /><b>Deepak Agarwal</b>, VP of Artificial Intelligence at <b>LinkedIn</b>: <i>"A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''</i><br /><br /><b>Vincent Pollet</b>, Head of Research at <b>Nuance</b>: <i>"The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''</i><br /><br /><b>Gareth James</b>, Professor of Data Sciences and Operations, co-author of the bestseller <b>An Introduction to Statistical Learning, with Applications in R</b>: <i>"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."</i><br /><br /><b>Everything you really need to know in Machine Learning in a hundred pages.</b><br /><br />This is the first of its kind <i>"read first, buy later"</i> book. You can find the book online, read it, and then come back to pay for it if you liked the book or found it useful for your work, business or studies.
The Model Thinker: What You Need to Know to Make Data Work for Yo...
by Scott E. Page

Language

English

Pages

398

Publication Date

November 27, 2018

Product Description
Customer Reviews
<div><b>How anyone can become a data ninja</b><br /><br /> From the stock market to genomics laboratories, census figures to marketing email blasts, we are awash with data. 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 <i>The Model Thinker</i>, 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. <i>The Model Thinker </i>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.<br /><br /></div>
Data Science from Scratch: First Principles with Python
by Joel Grus

Language

English

Pages

408

Publication Date

April 12, 2019

Product Description
Customer Reviews
<div><p>To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but 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.</p><p>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 the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.</p><ul><li>Get a crash course in Python</li><li>Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science</li><li>Collect, explore, clean, munge, and manipulate data</li><li>Dive into the fundamentals of machine learning</li><li>Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering</li><li>Explore recommender systems, natural language processing, network analysis, MapReduce, and databases</li></ul></div>
Programming: 3 Books in 1: Machine Learning + Python Machine Lear...
by Joseph Mining

Language

English

Pages

405

Publication Date

June 08, 2019

Product Description
Customer Reviews
<h2>★★<strong> Buy the Paperback Version of this Book and get the Kindle Book version for FREE </strong>★★</h2><h4><br /></h4><h2>Are you aware of the fact that the world of Innovation is rapidly changing? Are you interested in learning more about Machine Learning, Python Machine Learning and Python Programming? If you want to keep pace with Innovation then <u><em>keep reading…</em></u></h2><h3><br /></h3><h3>This guidebook is going to help you go <strong>from beginner to a professional</strong> in Python coding language in no time. When you are interested in learning more about what machine learning is all about, as well as <strong>how you can use a part of the coding from Python inside of this process</strong>, then this guidebook is the tool for you! Some of the topics that we will explore when we go through this guidebook will include:</h3><h3><br /></h3><ul><li><h3><strong>What is machine learning</strong>, and <u>Why</u> would a programmer want to learn how to use it?</h3></li><li><h3>Some of the basics of coding with Python and <strong>how to read the codes</strong> that we will work on;</h3></li><li><h3>The <strong>Reasons</strong> that many programmers are flocking to this coding language and eager to learn more;</h3></li><li><h3>Learning some of the building blocks that <u>will ensure your success</u> with machine learning.</h3></li><li><h3>How to set up the right environment in Python and <strong>get the libraries set up</strong>;</h3></li><li><h3>How K-Means clustering <u>is going to be different</u> from KNN;</h3></li><li><h3><strong>How to work</strong> with statistics and probability in order to understand more about machine learning.</h3></li><li><h3>What the generators are all about and <u>how to use them</u> to add some more strength to your own codes;</h3></li><li><h3>The difference between <strong>supervised</strong>, <strong>unsupervised</strong> and <strong>reinforcement learning</strong>.</h3></li><li><h3>And <strong>so much more</strong>!</h3></li></ul><p><br /></p><h3>The Python coding language is one of the best programming languages out there for both beginners and more experienced programmers to learn how to use. It has a lot of power, is easy to learn how to use and read, and even works with other coding languages, if that is what your program needs.</h3><h3>When you are ready to learn more about what machine learning is all about, and how you are able to benefit from it in your own coding and programming, make sure to check out this guidebook to help you get started!</h3><h3><br /></h3><h1>Scroll to the top of the page and select the <strong><em>buy now</em></strong> button!</h1>
Confident Data Skills: Master the Fundamentals of Working with Da...
by Kirill Eremenko

Language

English

Pages

269

Publication Date

January 03, 2018

Product Description
Customer Reviews
<p>Data science is the most exciting skill you can master. Data has dramatically changed how our world works. From entertainment to politics, from technology to advertising and from science to the business world, data is integral and its only limit is our imagination. If you want to have a vibrant and valuable professional life, being skilled with data is the key to a cutting-edge career. Learning how to work with data may seem intimidating or difficult but with <b><i>Confident Data Skills</i></b> you will be able to master the fundamentals and supercharge your professional abilities. This essential book covers data mining, preparing data, analysing data, communicating data, financial modelling, visualizing insights and presenting data through film making and dynamic simulations. </p><p>In-depth international case studies from a wide range of organizations, including Netflix, LinkedIn, Goodreads, Deep Blue, Alpha Go and Mike's Hard Lemonade Co. show successful data techniques in practice and inspire you to turn knowledge into innovation. <b><i>Confident Data Skills</i></b> also provides insightful guidance on how you can use data skills to enhance your employability and improve how your industry or company works through your data skills. Expert author and instructor, Kirill Eremenko, is committed to making the complex simple and inspiring you to have the confidence to develop an understanding, adeptness and love of data.</p>
Practical Statistics for Data Scientists: 50 Essential Concepts
by , Andrew Bruce

Language

English

Pages

318

Publication Date

May 10, 2017

Product Description
Customer Reviews
<div><p>Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.</p><p>Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.</p><p>With this book, you’ll learn:</p><ul><li>Why exploratory data analysis is a key preliminary step in data science</li><li>How random sampling can reduce bias and yield a higher quality dataset, even with big data</li><li>How the principles of experimental design yield definitive answers to questions</li><li>How to use regression to estimate outcomes and detect anomalies</li><li>Key classification techniques for predicting which categories a record belongs to</li><li>Statistical machine learning methods that “learn” from data</li><li>Unsupervised learning methods for extracting meaning from unlabeled data</li></ul></div>
Everybody Lies: Big Data, New Data, and What the Internet Can Tel...
by Seth Stephens-Davidowitz

Language

English

Pages

357

Publication Date

May 09, 2017

Product Description
Customer Reviews
<p>Foreword by Steven Pinker</p><p>Blending the informed analysis of <em>The Signal and the Noise</em> with the instructive iconoclasm of <em>Think Like a Freak</em>, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world—provided we ask the right questions.</p><p>By the end of an average day in the early twenty-first century, human beings searching the internet will amass eight trillion gigabytes of data. This staggering amount of information—unprecedented in history—can tell us a great deal about who we are—the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable. </p><p><em>Everybody Lies</em> offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women? </p><p>Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential—revealing biases deeply embedded within us, information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health—both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. <em>Everybody Lies</em> challenges us to think differently about how we see it and the world.</p>
Data Science for Business: What You Need to Know about Data Minin...
by , Tom Fawcett

Language

English

Pages

414

Publication Date

July 27, 2013

Product Description
Customer Reviews
<div><p>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.</p><p>Based on an MBA course Provost has taught at New York University over the past ten years, <i>Data Science for Business</i> 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.</p><ul><li>Understand how data science fits in your organization—and how you can use it for competitive advantage</li><li>Treat data as a business asset that requires careful investment if you’re to gain real value</li><li>Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way</li><li>Learn general concepts for actually extracting knowledge from data</li><li>Apply data science principles when interviewing data science job candidates</li></ul></div>
Access 2019 Bible.
by , Richard Kusleika

Language

English

Pages

1136

Publication Date

September 25, 2018

Product Description
Customer Reviews
<b>Master database creation and management</b> <p><i>Access 2019 Bible</i> is your, comprehensive reference to the world's most popular database management tool. With clear guidance toward everything from the basics to the advanced, this go-to reference helps you take advantage of everything Access 2019 has to offer. Whether you're new to Access or getting started with Access 2019, you'll find everything you need to know to create the database solution perfectly tailored to your needs, with expert guidance every step of the way. The companion website features all examples and databases used in the book, plus trial software and a special offer from Database Creations. Start from the beginning for a complete tutorial, or dip in and grab what you need when you need it.</p> <p>Access enables database novices and programmers to store, organize, view, analyze, and share data, as well as build powerful, integrable, custom database solutions — but databases can be complex, and difficult to navigate. This book helps you harness the power of the database with a solid understanding of their purpose, construction, and application.</p> <ul> <li>Understand database objects and design systems objects</li> <li>Build forms, create tables, manipulate datasheets, and add data validation</li> <li>Use Visual Basic automation and XML Data Access Page design</li> <li>Exchange data with other Office applications, including Word, Excel, and more</li> </ul> <p>From database fundamentals and terminology to XML and Web services, this book has everything you need to maximize Access 2019 and build the database you need.</p>

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