• Complain

Brett Lantz - Machine Learning with R: Expert techniques for predictive modeling

Here you can read online Brett Lantz - Machine Learning with R: Expert techniques for predictive modeling 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: Packt Publishing Limited, genre: Computer. 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.

Brett Lantz Machine Learning with R: Expert techniques for predictive modeling
  • Book:
    Machine Learning with R: Expert techniques for predictive modeling
  • Author:
  • Publisher:
    Packt Publishing Limited
  • Genre:
  • Year:
    2019
  • Rating:
    3 / 5
  • Favourites:
    Add to favourites
  • Your mark:
    • 60
    • 1
    • 2
    • 3
    • 4
    • 5

Machine Learning with R: Expert techniques for predictive modeling: summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Machine Learning with R: Expert techniques for predictive modeling" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

Solve real-world data problems with R and machine learningKey FeaturesThird edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.5 and beyondHarness the power of R to build flexible, effective, and transparent machine learning modelsLearn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett LantzBook DescriptionMachine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.This new 3rd edition updates the classic R data science book with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.What you will learnDiscover the origins of machine learning and how exactly a computer learns by examplePrepare your data for machine learning work with the R programming languageClassify important outcomes using nearest neighbor and Bayesian methodsPredict future events using decision trees, rules, and support vector machinesForecast numeric data and estimate financial values using regression methodsModel complex processes with artificial neural networks - the basis of deep learningAvoid bias in machine learning modelsEvaluate your models and improve their performanceConnect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlowWho this book is forData scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.

Brett Lantz: author's other books


Who wrote Machine Learning with R: Expert techniques for predictive modeling? Find out the surname, the name of the author of the book and a list of all author's works by series.

Machine Learning with R: Expert techniques for predictive modeling — 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 with R: Expert techniques for predictive modeling" 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
Machine Learning with R - Third Edition

Machine Learning with R - Third Edition

Copyright 2019 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Commissioning Editor: Vedika Naik

Acquisition Editor: Ben Renow-Clarke, Divya Poojari

Acquisition Editor - Peer Reviews: Suresh Jain

Project Editor: Radhika Atitkar

Content Development Editor: Joanne Lovell

Technical Editor: Saby D'silva

Proofreader: Safis Editing

Indexer: Tejal Daruwale Soni

Graphics: Sandip Tadge, Tom Scaria

Production Coordinator: Sandip Tadge

First published: October 2013

Second edition: July 2015

Third edition: April 2019

Production reference: 2160519

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78829-586-4

www.packtpub.com

maptio Mapt is an online digital library that gives you full access to over - photo 1

mapt.io

Mapt is an online digital library that gives you full access to over 5,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website.

Why subscribe?
  • Spend less time learning and more time coding with practical eBooks and Videos from over 4,000 industry professionals
  • Learn better with Skill Plans built especially for you
  • Get a free eBook or video every month
  • Mapt is fully searchable
  • Copy and paste, print, and bookmark content
Packt.com

Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.Packt.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at customercare@packtpub.com for more details.

At www.Packt.com, you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on Packt books and eBooks.

Contributors
About the authors

Brett Lantz (@DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at dataspelunking.com, a website dedicated to sharing knowledge about the search for insight in data.

This book could not have been written without the support of my family. In particular, my wife Jessica deserves many thanks for her endless patience and encouragement. My sons Will and Cal were born in the midst of the first and second editions, respectively, and supplied much-needed diversions while writing this edition. I dedicate this book to them in the hope that one day they are inspired to tackle big challenges and follow their curiosity wherever it may lead.

I am also indebted to many others who supported this book indirectly. My interactions with educators, peers, and collaborators at the University of Michigan, the University of Notre Dame, and the University of Central Florida seeded many of the ideas I attempted to express in the text; any lack of clarity in their expression is purely mine. Additionally, without the work of the broader community of researchers who shared their expertise in publications, lectures, and source code, this book might not exist at all. Finally, I appreciate the efforts of the R and RStudio teams and all those who have contributed to R packages, whose work have helped bring machine learning to the masses. I sincerely hope that my work is likewise a valuable piece in this mosaic.

About the reviewer

Raghav Bali is a Senior Data Scientist at one of the world's largest healthcare organization. His work involves research and development of enterprise level solutions based on machine learning, deep learning and natural language processing for healthcare and insurance related use cases. In his previous role at Intel, he was involved in enabling proactive data driven IT initiatives using natural language processing, deep learning and traditional statistical methods. He has also worked in finance domain with American Express, solving digital engagement and customer retention use cases.

Raghav has also authored multiple books with leading publishers, the recent one on latest advancements in transfer learning research.

Raghav has a master's degree (gold medalist) in Information Technology from International Institute of Information Technology, Bangalore. Raghav loves reading and is a shutterbug capturing moments when he isn't busy solving problems.

Preface

Machine learning, at its core, is concerned with algorithms that transform information into actionable intelligence. This fact makes machine learning well-suited to the present-day era of big data. Without machine learning, it would be nearly impossible to keep up with the massive stream of information.

Given the growing prominence of Ra cross-platform, zero-cost statistical programming environmentthere has never been a better time to start using machine learning. R offers a powerful but easy-to-learn set of tools that can assist you with finding the insights in your own data.

By combining hands-on case studies with the essential theory that you need to understand how things work under the hood, this book provides all the knowledge needed to start getting to work with machine learning.

Who this book is for

This book is intended for anybody hoping to use data for action. Perhaps you already know a bit about machine learning, but have never used R; or, perhaps you know a little about R, but are new to machine learning. In any case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic math and programming concepts, but no prior experience is required. All you need is curiosity.

What this book covers

, Introducing Machine Learning , presents the terminology and concepts that define and distinguish machine learners, as well as a method for matching a learning task with the appropriate algorithm.

Next page
Light

Font size:

Reset

Interval:

Bookmark:

Make

Similar books «Machine Learning with R: Expert techniques for predictive modeling»

Look at similar books to Machine Learning with R: Expert techniques for predictive modeling. 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 «Machine Learning with R: Expert techniques for predictive modeling»

Discussion, reviews of the book Machine Learning with R: Expert techniques for predictive modeling 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.