No Import Fees Deposit & $11.87 Shipping to Netherlands Details

Delivery Monday, October 23

Or fastest delivery Wednesday, October 18. Order within 15 hrs 56 mins

Only 7 left in stock (more on the way)

$$44.99 () Includes selected options. Includes initial monthly payment and selected options. Details

Payment

Secure transaction

We work hard to protect your security and privacy. Our payment security system encrypts your information during transmission. We don’t share your credit card details with third-party sellers, and we don’t sell your information to others. Learn more

Ships from

Amazon.com

Ships from

Amazon.com

Sold by

Amazon.com

Sold by

Amazon.com

Returns

Eligible for Return, Refund or Replacement within 30 days of receipt

Returns

Eligible for Return, Refund or Replacement within 30 days of receipt

This item can be returned in its original condition for a full refund or replacement within 30 days of receipt.

Read full return policy

Details

Add a gift receipt for easy returns

Delivery Monday, October 23. Order within 7 hrs 41 mins

Or fastest delivery Friday, October 20

Deliver to Netherlands

Used: Very Good | Details

Sold by Concept Uplift

Fulfilled by Amazon

Access codes and supplements are not guaranteed with used items.

Have one to sell?

Sell on Amazon

Image Unavailable

Image not available for
Color:

To view this video download Flash Player

Follow the Author

Peter Harrington

Machine Learning in Action First Edition

by Peter Harrington (Author)

4.0 76 ratings

See all formats and editions

Price

New from

Used from

eTextbook

"Please retry"

$34.99

—

Paperback, Illustrated

"Please retry"

$44.99

$4.49

eTextbook
$34.99 Read with our Free App
Paperback
$42.00 - $44.99

31 Used from $4.49 6 New from $44.99

{"desktop_buybox_group_1":[{"displayPrice":"$44.99","priceAmount":44.99,"currencySymbol":"$","integerValue":"44","decimalSeparator":".","fractionalValue":"99","symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"jtZ8ilk6eKCJOJY7uwD6HmM5JvnGXwSMqGjzH8TFBJZ2r%2BqJ4nAehGeRNWi%2BQ8MR8Z0jgbLaRr4fHp6DQeKHNyzb4%2F7K0sBL9czL89JBic6DwCMmATtEQIaF341xj3KVmT5kT8iPRmPYf8EU8bd2Cw%3D%3D","locale":"en-US","buyingOptionType":"NEW","aapiBuyingOptionIndex":0}, {"displayPrice":"$42.00","priceAmount":42.00,"currencySymbol":"$","integerValue":"42","decimalSeparator":".","fractionalValue":"00","symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"jtZ8ilk6eKCJOJY7uwD6HmM5JvnGXwSMsSzzHry77lrVmhCrhHTmX9Mb%2FWmQ7T3%2BS2mo75GMIYyPUU6pDnXC42Ts3KMGujAom4NlTlituNeAlYkliDMIORWWSx2Ix%2B5C%2Fboz9OWXHaTopqPqPDmzZo5FqUF%2FpZ9NM9PnnFK1vYAC6m5g5lXCWA%3D%3D","locale":"en-US","buyingOptionType":"USED","aapiBuyingOptionIndex":1}]}

Purchase options and add-ons

Summary

Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
About the Book
A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.
What's Inside

A no-nonsense introduction
Examples showing common ML tasks
Everyday data analysis
Implementing classic algorithms like Apriori and Adaboos

Table of Contents

PART 1 CLASSIFICATION
Machine learning basics
Classifying with k-Nearest Neighbors
Splitting datasets one feature at a time: decision trees
Classifying with probability theory: naïve Bayes
Logistic regression
Support vector machines
Improving classification with the AdaBoost meta algorithm
PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
Predicting numeric values: regression
Tree-based regression
PART 3 UNSUPERVISED LEARNING
Grouping unlabeled items using k-means clustering
Association analysis with the Apriori algorithm
Efficiently finding frequent itemsets with FP-growth
PART 4 ADDITIONAL TOOLS
Using principal component analysis to simplify data
Simplifying data with the singular value decomposition
Big data and MapReduce

ISBN-10

9781617290183
ISBN-13

978-1617290183
Edition

First Edition
Publisher

Manning
Publication date

April 19, 2012
Language

English
Dimensions

7.38 x 0.8 x 9.25 inches
Print length

384 pages
See all details

Frequently bought together

$44.99

Only 7 left in stock (more on the way).

Ships from and sold by Amazon.com.

$34.04

In Stock

Ships from and sold by Amazon.com.

Machine Learning for Hackers: Case Studies and Algorithms to Get You Started

$34.07

In Stock

Ships from and sold by Amazon.com.

Total price:

To see our price, add these items to your cart.

Try again!

Details

Added to Cart

Some of these items ship sooner than the others.

Show details Hide details

Choose items to buy together.

Andreas Müller
526
Paperback
$56.84
$11.69 shipping
Aurélien Géron
270
Paperback
$49.99
$14.56 shipping
Sebastian Raschka
253
Paperback
$46.79
$15.12 shipping
Laurence Moroney
185
Paperback
$38.49
$12.76 shipping
Sebastian Raschka
430
Paperback
$44.49
$14.77 shipping
Oliver Theobald
1,502
Paperback
$16.90
$9.70 shipping

Customer reviews

4 out of 5

76 global ratings

How customer reviews and ratings work

Reviews with images

See all photos

Sort reviews by

Top reviews from the United States

There was a problem filtering reviews right now. Please try again later.

TheKars

Great Book: A few things might help

Reviewed in the United States on April 27, 2022

Verified Purchase

This book is more details oriented and thorough than the other books I have read so far.
Chapter 1: How do you find K in KNN? is it the square root of the population size?
How do you find errors in KNN? The author has discussed only the accuracy metrics.
Is there any room for cost adjustment (like weighted distance measurement)?
Finally, I believe, KNN has a better application than handwriting identification because that works better with NN.
Chapter 5: While estimating the loss, simply used simple weight differences. A cross-entropy function could have been discussed.

Helpful

enty

Good Overview of Machine Learning

Reviewed in the United States on December 1, 2012

Verified Purchase

This book is a good introduction to the main algorithms used in machine learning: linear/logistics regression, kNN, decision and regression trees, naive Bayes, support vector machines, AdaBoost, SVD, and PCA. The author does a good job as presenting complex concepts in a simple fashion.
However, many chapters feel more like simplistic summaries around Python code and the editing can be poor at times repeating the same information between a main paragraph and a shaded summary on the next page. I would still recommend this book as a fairly broad overview of these techniques and a valuable starting point for implementing them. It is easy to read and offers a good selection of algorithms.
If you are looking for a more formal alternative, I can only recommend the book from Hastie, Tibshirani, and Friedman: The Elements of Statistical Learning . That book presents a more rigorous approach to the same algorithms, their goals, limitations and main variants. It is presented as a reference book but does not drown the reader in an sea of formulas, unlike similar reference books.

3 people found this helpful

Helpful

Ken L

Great book for getting into machine learning

Reviewed in the United States on July 4, 2012

Verified Purchase

This is a very good way to quickly and easily explore machine learning techniques. The code snippets are generally well explained, and short enough to be digestible. Being able to play around with the data and algorithms in an interactive way really helps the concepts sink in. I'm actually reading the book on my Kindle (the printed version includes a code to download an electronic version), and on my Kindle the book includes color figures and useful hyperlinks. As other reviewers have pointed out, some of the code examples have errors, which is frustrating. The book's website has an errors and corrections forum topic, but it's a pain to have to go there when the code does not work. However, the good (and there is much goodness) greatly outweighs the bad. If you like a hands-on approach to learning ML techniques (and don't mind chasing a few bugs) then you will love this book.

7 people found this helpful

Helpful

A "Just Do It" Machine Learning Book

Reviewed in the United States on January 19, 2013

Verified Purchase

I am new to Machine Learning and I found the book a very good hands-on introduction on the subject. The author takes 8 of the Top 10 algorithms in Machine Learning (based on a 2007 survey paper) and implements them in Python. Other reviewers have pointed out that the theoretical explanations and code quality were somewhat lacking, and thats true. However, even though Python is an extremely readable language, machine learning algorithms are (generally) hard, and I found that it helped to understand them better if I typed them out myself, copying/copy-pasting and restructuring the code as I went, and experimenting with the contents of the intermediate data structures in the REPL. Also, once you have a general idea of how it works, it becomes easier to parse the math in the paper on which the algorithm is based.

I still don't completely understand all the implementations, but the book did give me some intuition about how to choose the right algorithm for a given problem. I believe that is also important since ML practitioners often use third party algorithms rather than code everything up from scratch. Of course, for the times you do need to code it up from scratch, you can get some valuable insights about machine learning algorithm design from the style adopted in the book - start small, visualize in 2D/3D for insights, then generalize to higher dimensions. The examples cover a wide range, from dating sites to semiconductor plants, so you get a feel for all the different places these algorithms can be applied.

In short, if you want to "Just do ML", ie, quickly get started and pick up anything else you need along the way, then this book may be for you.

8 people found this helpful

Helpful

Atreya

Good attempt but needs LOT of improvement

Reviewed in the United States on June 17, 2012

Verified Purchase

Looking at many good reviews on amazon, I decided to purchase this book. It's a decent book, but IMO it has been edited poorly and the code has not been tested properly.

The introduction chapter got me really excited, just like other Manning's "in Action" books do. But once I started executing the code in chapter 2 "Classifying with k-nearest neighbors" I realized that the code had bugs. Though I could figure out what's wrong and fix the bugs, I did not expect this from Manning, after having read some of their excellent books like ( The Quick Python Book, Second Edition , Spring in Action and Hadoop in Action ).

Moreover the book has some introduction to python and numpy in appendix A. I believe the author could have pointed the reader elsewhere for learning python and those pages could have been used to explain more of numpy and matplotlib, which the author uses freely without any explanation in the text. (Yup, be ready to read some online numpy and matplotlib tutorials and documentation.)

If you don't know python, then you can do what I did: read The Quick Python Book, Second Edition and then attempt this book.

The figures in the book are not in color so you need to execute the code to understand what the author is telling. It forces you to actually run the code, which is good, but you can't read this book without a computer in front of you.

Finally, I am a big believer in following the conventions of a language. I would have been really happy had the author followed PEP8 ([...]), because along with learning machine learning, you could have learnt some good python coding practices.

36 people found this helpful

Helpful

Top reviews from other countries

Translate all reviews to English

Dr. Chrilly Donninger

Durchwachsene Einführung

Reviewed in Germany on June 24, 2012

Verified Purchase

Der Autor ist gelernter Elektronikingenieur. Er hat ein paar Jahre bei Intel gearbeitet. 2008 hat er sich - ohne zu inskribieren - erstmals in eine Statistikvorlesung gesetzt. Laut Buchrücken hat er bereits in zahlreichen akad. Journalen publiziert. Ich konnte kein ihm zuordenbares Werk ausfinding machen. Eine Anfrage im Verlagsforum blieb bisher unbeantwortet.

Ich vermute vielmehr, dass Harrington seine eigenen Lernschritte und praktischen Experimente zu einem Buch verarbeitet hat. Er überfordert den Leser jedenfalls nicht mit hochgestochener Theorie und Mathematik. Persönlich habe ich mich manchmal eher unterfordert gefühlt. Das Buch hat allerdings auch nur den Anspruch einer Einführung.

Die Auswahl der Algorithmen orientiert sich an den "Top 10 Algorithms in Datamining" ([1]). Er präsentiert im Buch davon acht. Die Auswahl ist plausibel. Sein eigener Beitrag ist die Programmierung von einfachen Varianten in Python. Nachdem ich noch nie in Python programmiert habe, kann ich die Qualität des Kodes nicht beurteilen. In Rezensionen auf Amazon.com wird er relativ heftig kritisiert. Auf der Verlagsseite gibt es jedenfalls einige errata. Kode ordentlich zu testen scheint heute bei Buchpublikationen nicht mehr üblich zu sein.

Die für die Algorithmen verwendeten Beispielanwendungen sind - so wie das gesamte Buch - durchwachsen. Bei 2 Algorithmen verwendet der Autor die Überlebenschance bei Pferdekoliken. Er räumt ein, dass er von Pferden keine Ahnung hat und daher die Ergebnisse nicht beurteilen kann. Einige konstruierte Beispiele sind eher kurios. Z.B. der Zusammenhang zwischen IQ und der Anzahl der Gänge bei einem Fahrrad. Sehr gut hat mir hingegen eine Untersuchung über Abstimmungsmuster von US-Abgeordneten gefallen. Die größte Stärke des Buches ist überhaupt die Auflistung einer Reihe von interessanten Datenquellen. Im Anhang geht er noch auf MapReduce und Hadoop ein. Damit kann man mit von amazon angemieteten Serverfarmen sehr grosse Datenmengen durchackern. Es ist aber mehr eine Werbeeinschaltung für das Hadoop in Action Buch.
Das Buch leidet auch etwas an den mässigen Grafiken. Es werden in Scatterplots Punkte aus verschiedenen Gruppen angezeigt. Der jeweilige Algorithmus soll die Gruppen separieren. Es ist aber kaum bis gar nicht erkennbar, zu welcher Gruppe ein Punkt gehört. Eine derartige Darstellung ist nur in Farbe sinnvoll. Gute Bücher (siehe [2]) verwenden dazu auch Farbgrafiken.

Prinzipiell finde ich die Betonung des praktischen Aspektes, sich mit Daten die Hände schmutzig zu machen, an diesem Buch sehr sympathisch. Die Ausführung hätte aber in einigen Details wesentlich besser sein können.

[1] Xindong Wu et al.: Top 10 Algorithms in Data Mining. Dieser - sehr bekannte Artikel - basiert auf einer Meinungsumfrage unter den Teilnehmern der IEEE International Conference on Datamining, Dec. 2006.
[2] Bishop Christoper M.: Pattern Recognition and Machine Learning.

16 people found this helpful

Translate review to English

Alan

Good Book for Those Starting Machine Learning.

Reviewed in India on November 6, 2018

Verified Purchase

The book is useful for those starting machine learning although the codes have not been updated to the latest python version so you may find looking up the latest alternatives for some codes.

Devendra joshi

Four Stars

Reviewed in India on October 2, 2017

Verified Purchase

Must read for all data scientists and analysts.

Amrit Dutta

Not a must read, good to read to scan the whole ML landscape.

Reviewed in India on June 9, 2018

Verified Purchase

Cons : This book has multiple targets - ML, MATH, PYTHON , thus failed to provide clear understanding of any one, Prior to reading this book i had good understanding of math , thus i can clearly tell nobody will understand the core mathematical concepts involved in the algos.

Pros: If you are starting afresh , then this book can tell you what you have to learn for ML. It has provided good analysis to dissect the landscape of ML.

One person found this helpful

Geevarghese samuel

Must buy for anyone wanting to take a deep dive into Machine Learning

Reviewed in India on February 2, 2017

Verified Purchase

A really good book that introduces ML algorithms. Many common ML algorithms are introduced and implemented. The book may appear a bit complex for someone who just started machine learning. Mix the contents of this book with some good courses online, and you are good to go.

The book is not for the impatient or faint hearts. The book shows actual implementation of various ML algorithms in Python using NumPy library.

2 people found this helpful

See more reviews

Shipping & Fee Details

Image Unavailable

Follow the Author