• Complain

Grus - Data Science from Scratch

Here you can read online Grus - Data Science from Scratch full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2019, publisher: OReilly Media, Inc, genre: Romance novel. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

Grus Data Science from Scratch
  • Book:
    Data Science from Scratch
  • Author:
  • Publisher:
    OReilly Media, Inc
  • Genre:
  • Year:
    2019
  • Rating:
    4 / 5
  • Favourites:
    Add to favourites
  • Your mark:
    • 80
    • 1
    • 2
    • 3
    • 4
    • 5

Data Science from Scratch: summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Data Science from Scratch" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

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. 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 todays messy glut of data. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability-and how and when theyre 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, Nave Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases.

Grus: author's other books


Who wrote Data Science from Scratch? Find out the surname, the name of the author of the book and a list of all author's works by series.

Data Science from Scratch — read online for free the complete book (whole text) full work

Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Data Science from Scratch" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

Light

Font size:

Reset

Interval:

Bookmark:

Make
Data Science from Scratch

by Joel Grus

Copyright 2019 Joel Grus. All rights reserved.

Printed in the United States of America.

Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.

OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .

  • Editor: Michele Cronin
  • Production Editor: Deborah Baker
  • Copy Editor: Rachel Monaghan
  • Proofreader: Rachel Head
  • Indexer: Judy McConville
  • Interior Designer: David Futato
  • Cover Designer: Karen Montgomery
  • Illustrator: Rebecca Demarest
  • April 2015: First Edition
  • May 2019: Second Edition
Revision History for the Second Edition
  • 2019-04-10: First Release

See http://oreilly.com/catalog/errata.csp?isbn=9781492041139 for release details.

The OReilly logo is a registered trademark of OReilly Media, Inc. Data Science from Scratch, Second Edition, the cover image of a rock ptarmigan, and related trade dress are trademarks of OReilly Media, Inc.

While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

978-1-492-04113-9

[LSI]

Preface to the Second Edition

I am exceptionally proud of the first edition of Data Science from Scratch.It turned out very much the book I wanted it to be. But severalyears of developments in data science, of progress in the Python ecosystem,and of personal growth as a developer and educator have changed what Ithink a first book in data science should look like.

In life, there are no do-overs. In writing, however, there are second editions.

Accordingly, Ive rewritten all the code and examples using Python 3.6(and many of its newly introduced features, like type annotations). Ive woven into the book an emphasis on writing clean code. Ive replaced some ofthe first editions toy examples with more realistic ones using realdatasets. Ive added new material on topics such as deep learning, statistics, andnatural language processing, corresponding to things that todays data scientistsare likely to be working with. (Ive also removed some material that seems less relevant.)And Ive gone over the book with a fine-toothed comb, fixing bugs, rewriting explanations thatare less clear than they could be, and freshening up some of the jokes.

The first edition was a great book, and this edition is even better. Enjoy!

  • Joel Grus
  • Seattle, WA
  • 2019
Conventions Used in This Book

The following typographical conventions are used in this book:

Italic

Indicates new terms, URLs, email addresses, filenames, and file extensions.

Constant width

Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.

Constant width bold

Shows commands or other text that should be typed literally by the user.

Constant width italic

Shows text that should be replaced with user-supplied values or by values determined by context.

Tip

This element signifies a tip or suggestion.

Note

This element signifies a general note.

Warning

This element indicates a warning or caution.

Using Code Examples

Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/joelgrus/data-science-from-scratch.

This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless youre reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from OReilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your products documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: Data Science from Scratch, Second Edition, by Joel Grus (OReilly). Copyright 2019 Joel Grus, 978-1-492-04113-9.

If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at .

OReilly Online Learning
Note

For almost 40 years, OReilly Media has provided technology and business training, knowledge, and insight to help companies succeed.

Our unique network of experts and innovators share their knowledge and expertise through books, articles, conferences, and our online learning platform. OReillys online learning platform gives you on-demand access to live training courses, in-depth learning paths, interactive coding environments, and a vast collection of text and video from OReilly and 200+ other publishers. For more information, please visit http://oreilly.com.

How to Contact Us

Please address comments and questions concerning this book to the publisher:

  • OReilly Media, Inc.
  • 1005 Gravenstein Highway North
  • Sebastopol, CA 95472
  • 800-998-9938 (in the United States or Canada)
  • 707-829-0515 (international or local)
  • 707-829-0104 (fax)

We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at http://bit.ly/data-science-from-scratch-2e.

To comment or ask technical questions about this book, send email to .

For more information about our books, courses, conferences, and news, see our website at http://www.oreilly.com.

Find us on Facebook: http://facebook.com/oreilly

Follow us on Twitter: http://twitter.com/oreillymedia

Watch us on YouTube: http://www.youtube.com/oreillymedia

Acknowledgments

First, I would like to thank Mike Loukides for accepting my proposal for this book (and for insisting that I pare it down to a reasonable size). It would have been very easy for him to say, Whos this person who keeps emailing me sample chapters, and how do I get him to go away? Im grateful he didnt. Id also like to thank my editors, Michele Cronin and Marie Beaugureau, for guiding me through the publishing process and getting the book in a much better state than I ever would have gotten it on my own.

I couldnt have written this book if Id never learned data science, and I probably wouldnt have learned data science if not for the influence of Dave Hsu, Igor Tatarinov, John Rauser, and the rest of the Farecast gang. (So long ago that it wasnt even called data science at the time!) The good folks at Coursera and DataTau deserve a lot of credit, too.

Next page
Light

Font size:

Reset

Interval:

Bookmark:

Make

Similar books «Data Science from Scratch»

Look at similar books to Data Science from Scratch. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.


Reviews about «Data Science from Scratch»

Discussion, reviews of the book Data Science from Scratch and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.