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Machine Learning with R [Kindle Edition]

Brett Lantz
5.0 von 5 Sternen  Alle Rezensionen anzeigen (2 Kundenrezensionen)

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Produktbeschreibungen

Kurzbeschreibung

In Detail

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data.

"Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions.

How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.

We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.

"Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.

Approach

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.

Who this book is for

Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

Über den Autor und weitere Mitwirkende

Brett Lantz

Brett Lantz has spent the past 10 years using innovative data methods to understand human behavior. A sociologist by training, he was first enchanted by machine learning while studying a large database of teenagers' social networking website profiles. Since then, he has worked on interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others. When he's not spending time with family, following college sports, or being entertained by his dachshunds, he maintains dataspelunking.com, a website dedicated to sharing knowledge about the search for insight in data.


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5.0 von 5 Sternen Highlight 5. Januar 2015
Format:Kindle Edition|Verifizierter Kauf
my favorite machine learning book. read it a few times and still like it a lot.
Very clear & useful
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0 von 1 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Great introduction into Machine Learning 28. März 2014
Von Lucas
Format:Taschenbuch|Verifizierter Kauf
I'm quite proficient in R, but hadn't heard about Machine Learning at all before I swallowed up this book within a mere two weeks!
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Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)
Amazon.com: 4.5 von 5 Sternen  40 Rezensionen
78 von 81 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Just fine 30. Januar 2014
Von Dimitri Shvorob - Veröffentlicht auf Amazon.com
Format:Kindle Edition
Some of the other reviewers seem to be unclear on the concept of "introduction", demanding rigor and depth - and, unfortunately, not suggesting any alternatives. If you want deep understanding of the algorithms, you will need to start with a proper textbook, like "Pattern matching" by Bishop, or "Elements of statistical learning" by Hastie and Tibshirani. If you want a more accessible, high-level presentation with examples that can be reproduced, in R, you want to get "Introduction to statistical learning" by James, Witten, Hastie and Tibshirani. If you want a *really* accessible introduction to the techniques - again, with examples that you can try and build on - well, "Machine learning with R" just might be the best choice today. (On R side; if you have invested in Python, Peter Harrington's "Machine learning in action" and books by Wes McKinney are a good bet). OK, this is not an outstanding book, it is under-edited and plain-looking - unfortunately, Packt follow the no-frills approach of O'Reilly - but it is friendly, reasonably well written, and offers a good deal of content. (Extra brownie points for Chapter 11). Let me put it this way: you want to read "Introduction to statistical learning", but "Machine learning with R" is a good warm-up.
30 von 31 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Less tech speak, more meaty 7. April 2014
Von Amazon Customer - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Verifizierter Kauf
First off, I am newbie to both machine learning and R and wanted find a starting point somewhere. I browsed around many books before deciding on this one. The writing style of Mr. Lantz is provided in a very understandable/readable manner. It's akin to someone sitting next to you and explaining things in a down to earth, layman's fashion rather than try to "tech speak" you to death with complicated explanations (aka formal textbook). Just the right amount of hand holding for me. I highlight quite bit and it's actually difficult with this book as there isn't much fluff. He's very succinct. The books states that it's for someone who know some ML and no R or R and no ML. I don't know either and the material is digestible except for one thing: review your stats! I took statistics long ago in college and never really learned it well the first time so I had stop and reread core concepts before continuing. Do yourself a favor and review basic statistics and probability before you start this book. I read both "Naked Statistics" and "Statistics in Plain English" and it helped me a great deal (and probably will continue to do so since it appears a bulk of machine learning is stats and prob). Currently into about a third of the way in and I am finding it to be very enjoyable and practical. Other reviewers point out that this book is too basic and this may be the case, but for someone like me who is starting from absolute scratch and who needs to understand basic ML concepts (AND basic R) I find it a great book. Will post an addendum once I complete it.
39 von 45 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Already obsolete 16. Juni 2014
Von K. Parent - Veröffentlicht auf Amazon.com
Format:Kindle Edition|Verifizierter Kauf
This book uses R packages that are have been updated since its publication and no longer work with the code given in the book. I contacted the publisher, but because the code works fine with the package versions it was written for, they will not offer updates on their website. If you know machine learning and R well, you can probably figure out a workaround, but you're also not the intended audience for this book.
26 von 31 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Excellent book 20. März 2014
Von cool_einstein - Veröffentlicht auf Amazon.com
Format:Kindle Edition|Verifizierter Kauf
This is a great book. I liked the way authors highlight syntax for models and discuss strengths and weeknesses. It has a nice balance of theory and hands-on training. However, I would need to use a R book, such as R in action, in conjunction with it.

I have looked at many books on the topic. I will put my review for all of these. Perhaps this can save you some time.
1) http://www.amazon.com/dp/0470650931 : Good theoretical book, but badly written and does not have any hands on exercise.
2) http://www.amazon.com/dp/1466503963 : This is another great book. Good balance of theory and hands-on exercise. This is an excellent book to start learning data mining and R. However, this book relies on a GUI RCommander. It does a good job and one can do a lot with it but it has its limitations. However, I will still use this book.
3) http://www.amazon.com/dp/1439810184 : This is an advanced book and heavily entrinched in cases. This makes it difficult to replicate things unless your work is directly related to one of the case studies covered.
4) http://www.amazon.com/dp/0133412938 : Good examples, but does not explain much about the interpretation. This leaves one wondering what is the purpose of certain graph, what are the axis and how to interpret it. if appropriate explanation is added, this would be an excellent book.
5) http://www.amazon.com/dp/111844714X : This book is very expensive and almost totally devoid of any theory or discussion. I would not use it.
6) http://www.amazon.com/dp/1441998896: This is a decent book. It relies on another GUI, Rattle. It is a strong contender to the book 2 in this list.
12 von 14 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Excellent Introduction to both Machine Learning and R 14. Februar 2014
Von Dr. Christian B. Smart - Veröffentlicht auf Amazon.com
Format:Kindle Edition|Verifizierter Kauf
If you are new to both machine learning and R and want to learn both at the same time, I can't imagine there being a better book.

I needed to figure out how to implement nearest neighbors, decision trees, SVM, neural networks, and boosting on two data sets, in a short amount of time. I had no experience with R and my only prior experience with machine learning was neural networks. Using this book I was able to implement four algorithms in R. For each topic the book describes an application, the algorithm, provides code to implement the algorithm. You can download the data set from the publisher's website so you can try it out.
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