Matt Harrison - Machine Learning Pocket Reference: Working with Structured Data in Python
Here you can read online Matt Harrison - Machine Learning Pocket Reference: Working with Structured Data in Python full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. City: Sebastopol, CA, year: 2019, publisher: O’Reilly Media, genre: Computer / Science. 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.
- Book:Machine Learning Pocket Reference: Working with Structured Data in Python
- Author:
- Publisher:O’Reilly Media
- Genre:
- Year:2019
- City:Sebastopol, CA
- Rating:3 / 5
- Favourites:Add to favourites
- Your mark:
Machine Learning Pocket Reference: Working with Structured Data in Python: summary, description and annotation
We offer to read an annotation, description, summary or preface (depends on what the author of the book "Machine Learning Pocket Reference: Working with Structured Data in Python" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.
Matt Harrison: author's other books
Who wrote Machine Learning Pocket Reference: Working with Structured Data in Python? Find out the surname, the name of the author of the book and a list of all author's works by series.
Machine Learning Pocket Reference: Working with Structured Data in Python — 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 "Machine Learning Pocket Reference: Working with Structured Data in Python" 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.
Font size:
Interval:
Bookmark:
by Matt Harrison
Copyright 2019 Matt Harrison. 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 .
- Acquisitions Editor: Rachel Roumeliotis
- Development Editor: Nicole Tache
- Production Editor: Christopher Faucher
- Copyeditor: Sonia Saruba
- Proofreader: Christina Edwards
- Indexer: WordCo Indexing Services, Inc.
- Interior Designer: David Futato
- Cover Designer: Karen Montgomery
- Illustrator: Rebecca Demarest
- September 2019: First Edition
- 2019-08-27: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781492047544 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Machine Learning Pocket Reference, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
The views expressed in this work are those of the author, and do not represent the publishers views. 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.
978-1-492-04754-4
[LSI]
Machine learning and data science are very popular right now andare fast-moving targets. I have worked with Python and data formost of my career and wanted to have a physical book thatcould provide a reference for the common methods that I havebeen using in industry and teaching during workshops to solvestructured machine learning problems.
This book is what I believe is the best collection of resourcesand examples for attacking a predictive modeling task if youhave structured data. There are many libraries that perform aportion of the tasks required and I have tried to incorporatethose that I have found useful as I have applied these techniquesin consulting or industry work.
Many may lament the lack of deep learning techniques. Thosecould be a book by themselves. I also prefer simpler techniquesand others in industry seem to agree. Deep learning for unstructureddata (video, audio, images), and powerful tools like XGBoost forstructured data.
I hope this book serves as a useful reference for you to solvepressing problems.
This book gives in-depth examples of solving common structured dataproblems. It walks through various libraries and models, theirtrade-offs, how to tune them, and how to interpret them.
The code snippets are meant to be sized such that you can useand adapt them in your own projects.
If you are just learning machine learning, or have worked withit for years, this book should serve as a valuable reference. It assumessome knowledge of Python, and doesnt delve at all into syntax.Rather it shows how to use various libraries to solve real-worldproblems.
This will not replace an in-depth course, but should serve as areference of what an applied machine learning course might cover.(Note: The author uses it as a reference for the data analyticsand machine learning courses he teaches.)
The following typographical conventions are used in this book:
ItalicIndicates new terms, URLs, email addresses, filenames, and file extensions.
Constant widthUsed 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.
This element signifies a tip or suggestion.
This element signifies a general note.
This element indicates a warning or caution.
Supplemental material (code examples, exercises, etc.) is available at https://github.com/mattharrison/ml_pocket_reference.
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: Machine Learning Pocket Reference by Matt Harrison (OReilly). Copyright 2019 Matt Harrison, 978-1-492-04754-4.
If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at .
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.
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://www.oreilly.com/catalog/9781492047544.
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
Much thanks to my wife and family for their support. Im grateful to the Python communityfor providing a wonderful language and toolset to work with. Nicole Tache has been lovely to work with and provided excellent feedback. My technical reviewers, Mikio Braun, Natalino Busa, and Justin Francis, kept me honest. Thanks!
Font size:
Interval:
Bookmark:
Similar books «Machine Learning Pocket Reference: Working with Structured Data in Python»
Look at similar books to Machine Learning Pocket Reference: Working with Structured Data in Python. 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.
Discussion, reviews of the book Machine Learning Pocket Reference: Working with Structured Data in Python 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.