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The Hundred-Page Machine Learning Book
by Andriy Burkov

Language

English

Pages

Publication Date

January 12, 2019

Product Description
Customer Reviews
<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.
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 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>
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>
SQL in 10 Minutes, Sams Teach Yourself: Sams Teac Your SQL 10 Min...
by Ben Forta

Language

English

Pages

287

Publication Date

October 25, 2012

Product Description
Customer Reviews
Sams Teach Yourself SQL in 10 Minutes, Fourth Edition New full-color code examples help you see how SQL statements are structured Whether you're an application developer, database administrator, web application designer, mobile app developer, or Microsoft Office users, a good working knowledge of SQL is an important part of interacting with databases. And Sams Teach Yourself SQL in 10 Minutes offers the straightforward, practical answers you need to help you do your job. Expert trainer and popular author Ben Forta teaches you just the parts of SQL you need to know–starting with simple data retrieval and quickly going on to more complex topics including the use of joins, subqueries, stored procedures, cursors, triggers, and table constraints. You'll learn methodically, systematically, and simply–in 22 short, quick lessons that will each take only 10 minutes or less to complete. With the Fourth Edition of this worldwide bestseller, the book has been thoroughly updated, expanded, and improved. Lessons now cover the latest versions of IBM DB2, Microsoft Access, Microsoft SQL Server, MySQL, Oracle, PostgreSQL, SQLite, MariaDB, and Apache Open Office Base. And new full-color SQL code listings help the beginner clearly see the elements and structure of the language. 10 minutes is all you need to learn how to... Use the major SQL statements Construct complex SQL statements using multiple clauses and operators Retrieve, sort, and format database contents Pinpoint the data you need using a variety of filtering techniques Use aggregate functions to summarize data Join two or more related tables Insert, update, and delete data Create and alter database tables Work with views, stored procedures, and more Table of Contents 1 Understanding SQL 2 Retrieving Data 3 Sorting Retrieved Data 4 Filtering Data 5 Advanced Data Filtering 6 Using Wildcard Filtering 7 Creating Calculated Fields 8 Using Data Manipulation Functions 9 Summarizing Data 10 Grouping Data 11 Working with Subqueries 12 Joining Tables 13 Creating Advanced Joins 14 Combining Queries 15 Inserting Data 16 Updating and Deleting Data 17 Creating and Manipulating Tables 18 Using Views 19 Working with Stored Procedures 20 Managing Transaction Processing 21 Using Cursors 22 Understanding Advanced SQL Features Appendix A: Sample Table Scripts Appendix B: Working in Popular Applications Appendix C : SQL Statement Syntax Appendix D: Using SQL Datatypes Appendix E: SQL Reserved Words
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>
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>
The Data Warehouse Toolkit: The Definitive Guide to Dimensional M...
by , Margy Ross

Language

English

Pages

600

Publication Date

July 01, 2013

Product Description
Customer Reviews
<p><b>Updated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence!</b></p> <p>The first edition of Ralph Kimball's <i>The Data Warehouse Toolkit</i> introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more.</p> <ul> <li>Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence</li> <li>Begins with fundamental design recommendations and progresses through increasingly complex scenarios</li> <li>Presents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and more</li> <li>Draws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and more</li> </ul> <p>Design dimensional databases that are easy to understand and provide fast query response with <i>The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition</i>.</p>
Data Analytics Made Accessible: 2019 edition
by Anil Maheshwari

Language

English

Pages

317

Publication Date

May 01, 2014

Product Description
Customer Reviews
This book fills the need for a concise and conversational book on the hot and growing field of Data Science. Easy to read and informative, this lucid book covers everything important, with concrete examples, and invites the reader to join this field. The chapters in the book are organized for a typical one-semester course. The book contains case-lets from real-world stories at the beginning of every chapter. There is also a running case study across the chapters as exercises. This book is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms. Finally, it includes a tutorial for R. <br />The 2019 edition contains expanded primers on Big Data, Artificial Intelligence, and Data Science careers. For the first time, it now includes a full tutorial on Python. <br />The book has proved very popular throughout the world. Dozens of universities around the world have adopted it as a textbook for their courses. Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others attracted to the idea of discovering new insights and ideas from data can use this as a textbook. Professionals in various domains, including executives, managers, analysts, professors, doctors, accountants, and others can use this book to learn in a few hours how to make sense of and develop actionable insights from the enormous data coming their way. This is a flowing book that one can finish in one sitting, or one can return to it again and again for insights and techniques.<br />Table of Contents<br />Chapter 1: Wholeness of Data Analytics<br />Chapter 2: Business Intelligence Concepts & Applications<br />Chapter 3: Data Warehousing<br />Chapter 4: Data Mining<br />Chapter 5: Data Visualization<br />Chapter 6: Decision Trees<br />Chapter 7: Regression Models<br />Chapter 8: Artificial Neural Networks<br />Chapter 9: Cluster Analysis <br />Chapter 10: Association Rule Mining <br />Chapter 11: Text Mining<br />Chapter 12: Naïve Bayes Analysis<br />Chapter 13: Support Vector Machines<br />Chapter 14: Web Mining<br />Chapter 15: Social Network Analysis<br />Chapter 16: Big Data<br />Chapter 17: Data Modeling Primer<br />Chapter 18: Statistics Primer<br />Chapter 19: Artificial Intelligence Primer<br />Chapter 20: Data Science Careers<br />Appendix R: Data Mining Tutorial using R<br />Appendix P: Data Mining Tutorial using Python
Python Programming: 3 Books in 1: Ultimate Beginner’s, Intermed...
by Ryan Turner

Language

English

Pages

274

Publication Date

November 18, 2018

Product Description
Customer Reviews
<h2>If you’re looking for a way to become an expert coder and impress your friends with the programs you can make from scratch, then keep reading.</h2><br /><br />Here's the deal. You’ve decided that one of the most in-demand skills is the best place to start when making money. However, learning how to code can be a very long and arduous process. However not learning it and hiring a programmer can be very expensive. You may want to build an app or code a website but the costs have always been too high, making it pointless and not very cost effective. Sound familiar?<br /><br />If it does, then the information inside this book is your answer. You will be given all the tips, tricks, and practice codes that you need to learn Python, the solid programming language used in hundreds of industries around the world. This information allows you to become skilled much faster and perfect your coding skills in no time. Imagine cutting months off of your learning curve and get a strong base of knowledge in no time at all. Imagine getting your project done yourself for a fraction of the cost. <br /><br />This all is possible with the help of this <strong>three-book bundle, beginners, intermediate and expert guides!</strong><br /><br />Don’t be fooled by other books on the market. They only take a brief look into Python, showing some of the topics but never going deep into the topics and actually showing you how to work on the code and make it yours. This guidebook goes more in depth about the Python language. This is detailed, scientific information compiled together by experts in an easy to read fashion.<br /><br /><h2>In this Python guide, you will discover:</h2><br /><br /><strong>Book 1:</strong><br /><br />• The benefits of python<br />• How to get up and running with python<br />• Full instructions of how to code<br />• How to make predictions with algorithms<br />• Real world examples of Python<br />• The 3 different examples of coding<br />…and so much more!<br /><br /><strong>Book 2:</strong><br /><br />• The importance of Machine Learning<br />• The Basics of Working with Python <br />• How to Setting up Your Python Environment<br />• Data Preprocessing with Machine Learning<br />• Working with Linear Regression in Machine Learning<br />• The Benefits of a Decision Tree <br />• Working on the Random Forest Algorithm<br />• The Advantages of the Naive Bayes Algorithm<br />• How Can you Use the KNN Algorithm<br />…And so much more !<br /><br /><strong>Book 3:</strong><br /><br />• The best benefits of Python and why programmers around the world choose it<br />• How to download the Python language on your computer, regardless of the operating system you prefer<br />• How to write your first program in Python<br />• What is means to work with an object-oriented programming language<br />• How to write conditional statements, loops, functions, variables, classes, exceptions, and more<br />• How to read some of the errors that may show up in your code<br />• How to troubleshoot your own codes<br />• Sample projects that walk you through how to complete them and use many of the tools and techniques you have learned through this guidebook<br /><br /><h2>If you want to learn more about how to get the best Python training and if you are ready to write your own codes and turn your ideas into reality, then simply click the buy now button on this page to get started.</h2><br /><br />

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