Puedes empezar a leer Machine Learning for Hackers en tu Kindle en menos de un minuto. ¿No tienes un Kindle? Consigue un Kindle aquí o empieza a leer ahora con una de nuestras aplicaciones de lectura Kindle gratuitas.

Enviar a mi Kindle o a otro dispositivo

 
 
 

Pruébalo gratis

Lee el principio de este eBook gratis

Enviar a mi Kindle o a otro dispositivo

Cualquiera puede leer libros Kindle, incluso sin un dispositivo Kindle, con la aplicación gratuita Kindle para smartphones y tablets.
Machine Learning for Hackers
 
Ampliar la imagen
 

Machine Learning for Hackers [Versión Kindle]

Drew Conway , John Myles White
5.0 de un máximo de 5 estrellas  Ver todas las opiniones (1 opinión de cliente)

Precio lista ed. digital: EUR 24,71 ¿Qué es esto?
Precio lista ed. impresa: EUR 33,28
Precio Kindle: EUR 17,30 IVA incluido (si corresponde) y envío a través de Amazon Whispernet
Ahorras: EUR 15,98 (48%)

Formatos

Precio Amazon Nuevo desde Usado desde
Versión Kindle EUR 17,30  
Tapa blanda EUR 31,62  
Celebra el Mes del libro
Hasta -40%* en una selección de libros en inglés. * Ver condiciones.

Los clientes que compraron este producto también compraron


Descripción del producto

Descripción del producto

If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.

Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.

  • Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text
  • Use linear regression to predict the number of page views for the top 1,000 websites
  • Learn optimization techniques by attempting to break a simple letter cipher
  • Compare and contrast U.S. Senators statistically, based on their voting records
  • Build a “whom to follow” recommendation system from Twitter data

Detalles del producto

  • Formato: Versión Kindle
  • Tamaño del archivo: 6857 KB
  • Longitud de impresión: 324
  • Uso simultáneo de dispositivos: Sin límite
  • Editor: O'Reilly Media; Edición: 1 (13 de febrero de 2012)
  • Vendido por: Amazon Media EU S.à r.l.
  • Idioma: Inglés
  • ASIN: B007A0BNP4
  • Texto a voz: Activado
  • X-Ray:
  • Valoración media de los clientes: 5.0 de un máximo de 5 estrellas  Ver todas las opiniones (1 opinión de cliente)
  • Clasificación en los más vendidos de Amazon: n°74.902 Pagados en Tienda Kindle (Ver el Top 100 de pago en Tienda Kindle)

Opiniones de clientes

4 estrellas
0
3 estrellas
0
2 estrellas
0
1 estrellas
0
5.0 de un máximo de 5 estrellas
5.0 de un máximo de 5 estrellas
Las opiniones de cliente más útiles
5.0 de un máximo de 5 estrellas Genial introducción al mundo del Machine Learning 23 de febrero de 2013
Formato:Tapa blanda|Compra verificada por Amazon
El libro es una muy buena introducción al Machine Learning y los conceptos en los que se basa. Lo recomiendo para todo aquel desarrollador que quiera introducirse en ese mundo.
¿Esta opinión te ha parecido útil?
Opiniones de clientes más útiles en Amazon.com (beta)
Amazon.com: 3.2 de un máximo de 5 estrellas  24 opiniones
119 de 127 personas piensan que la opinión es útil
3.0 de un máximo de 5 estrellas Machine Learning for Non-Hackers 21 de marzo de 2012
Por Voracious Reader - Publicado en Amazon.com
Formato:Tapa blanda|Compra verificada por Amazon
By page count, this is primarily a book on R, with some additional time spent on machine learning.

There is way too much time spent on R, dedicated to such things as parsing email messages, and spidering webpages, etc. These are things that no-one with other tools available would do in R. And it's not that it's easier to do it in R, it's actually harder than using an appropriate library, like JavaMail. And yet, while much time is spent in details, like regexes to extract dates (ick!), more interesting R functions are given short shrift.

There's some good material in here, but it's buried under the weight of doing everything in R. If you are a non-programmer, and want to use only one hammer for everything, then R is not a bad choice. But it's not a good choice for developers that are already comfortable with a wider variety of tools.

I'd recommend Programming Collective Intelligence by Segaran, if you would describe yourself as a "Hacker".
44 de 45 personas piensan que la opinión es útil
3.0 de un máximo de 5 estrellas Not for a hacker, probably for a scientist 4 de mayo de 2012
Por Sharon Talbot - Publicado en Amazon.com
Formato:Tapa blanda
In Machine Learning for Hackers by Drew Conway and John Myles White, the reader is introduced to a number of techniques useful for creating systems that can understand and make use of data. While the book has solid topical material and is written in a fluid and easy to read manner, I don't feel that this book is really for hackers, unless the definition of hacker is vastly different from "programmer".

Much of the text is taken up explaining how to parse strings, change dates, and otherwise munge data into shape to be operated on by statistical functions provided by R. In fact, there is so much of the book in that fashion that I end up skipping through large portions to get back to something that is worth spending time reading about. I can't understand why a programmer would need significant education in string parsing. I was also put off by the vast amount of text explaining basic statistics. Maybe a recent computer science graduate is simply the wrong reader for this book?

I think it is certainly possible to learn the basic principles of machine hacking from this book, and even to put them to good use with R in the same manner displayed in the examples. Indeed, the code and data available for this book would be very useful as prep for an introductory course at an academic institution. To make the best use of the text, you really should be sitting at your computer, reading the text side by side with the code, and operating on the data with R as instructed to do.

Personally, I found that wading through this text wasn't enjoyable it due to the lack of density of material at the depth I was looking for. Other readers may find it is just right for them, but I suspect those readers would not be hackers, contrary to the implication of the title. As best as I can figure, this book would best serve a student scientific researcher who wanted to understand what machine learning was about, and did not have significant prior experience in programming or statistics. Alternatively, if you are significantly distant in years from your time in statistics, or considered learning R one of your goals, this book could work well for you.

I received this book for free as part of the O'Reilly Blogger Review program, which is neat.

I should note that I read this book on the iPhone as an ePub. There were some formatting problems with tables that were distracting, but otherwise it was readable.
44 de 47 personas piensan que la opinión es útil
2.0 de un máximo de 5 estrellas Erroneous but entertaining 27 de julio de 2012
Por Kurt - Publicado en Amazon.com
Formato:Tapa blanda
I used this book to teach students about data mining and machine learning with a hands-on approach. I intended it to be used as a book for the students to rely and fall back on. It is not suited well for that purpose.

Pros: The book is affordable and nicely written. The authors take great care in making the book useful and entertaining and one can immediately start putting things into practise. Also, the R examples are interesting and by itself motivating.

Cons: The book has a couple of very grievous errors, that make me wonder the authors understand the subject matter. This is especially striking in the chapters on PCA and Multidimensional Scaling (which I covered in some depth in the class), but also to a lesser degree in other parts of the book that I have read more thoroughly (like optimization and linear and nonlinear regression). Many errors are not typos or simple mistakes but seem to be proof of a profound misunderstanding of concepts by the authors. I am sorry to be so blunt, but one should not write a book about topics that one is not intimate with. Given that the book is probably quite successful, it propagates error into a community whose members may not have the statistical background to spot the errors immediately. Some methods used in the book are quite hard to understand even for graduate students and to be so nonchalant about the underlying theory can be dangerous. I realize that the book is intended to be superficial with regards to mathematical or conceptual depth, but this combined with some of the presented high-level techniques can easily backfire when people are given the tools, but not the understanding. Especially when the explanations on interpretation are plainly wrong (I am talking about using standard deviations instead of variances, substantive interpretation of methodological artifacts, wrong explanation of R output, etc.). Additionally, certain parts of the book became outdated as soon as the book came out, such as the Google example.

Overall, I do not recommend the book. I now only use it as a collection of nice examples and sometimes borrow bits of their R code.
32 de 39 personas piensan que la opinión es útil
5.0 de un máximo de 5 estrellas Excellent and immediately practical, if you already know some R 20 de febrero de 2012
Por Ravi Aranke - Publicado en Amazon.com
Formato:Tapa blanda
I started my journey in the machine learning / data mining field thanks to curiosity generated by Toby Segaran's classic Programming Collective Intelligence: Building Smart Web 2.0 Applications. The book by Drew Conway and John White continues in the same excellent tradition. It presents case studies which are interesting enough that you can appreciate them without too much domain knowledge and without getting too deep into technical nitty-gritty. At the same time, the case studies are meaty enough that you can adapt them to real life problems and hack together a quick working prototype in your practice.

By many estimates (and my own experience), 80% of time in machine learning is spent in data cleaning and exploratory data analysis. This book has very good coverage of both areas. Authors use Hadley Wickham's excellent packages viz. ggplot2, plyr and reshape2. If you are doing serious exploratory data analysis in R, these packages are a must and the book does a great job in showing them in action.

The reason I suffixed the review with 'if you know a little R' is that data cleansing requires one to be fairly comfortable with somewhat arcane R syntax. If you don't know any R at all, it would be helpful to get a more gentle introduction - such as R Cookbook (O'Reilly Cookbooks) - before you tackle this book.

In summary, this is an excellent 2nd book on R to have as you try to deploy machine learning in real life.
BTW, if you are looking for 3rd R book, my vote is Data Mining with R: Learning with Case Studies (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
6 de 7 personas piensan que la opinión es útil
2.0 de un máximo de 5 estrellas Too many errors, code doesn't even work correctly 21 de diciembre de 2012
Por Michael Flynn - Publicado en Amazon.com
Formato:Tapa blanda
This book is an attempt to give someone an introduction to the very, very basic concepts used in Machine Learning, using R. Unfortunately he spends about 1/3 of the book just talking about R and not about Machine Learning. My biggest complaint, however, is that the sample code that is supposed to demonstrate the concepts doesn't work and needs correcting. I wouldn't recommend this book to someone who desires to learn Machine Learning through coding.
Ir a Amazon.com para ver las 24 opiniones existentes 3.2 de un máximo de 5 estrellas

Subrayados populares

 (¿Qué es esto?)
&quote;
machine learning techniques are also referred to as pattern recognition algorithms &quote;
Subrayado por 8 usuarios de Kindle
&quote;
training set comes from, referring to the set of data used to build a machine learning process. &quote;
Subrayado por 5 usuarios de Kindle
&quote;
Machine learning is concerned with teaching computers something about the world, so that they can use that knowledge to perform other tasks. &quote;
Subrayado por 5 usuarios de Kindle

Buscar productos similares por categoría


ARRAY(0xa9b85e40)