Send a Visa virtual eGift card.
Add Prime to get Fast, Free delivery
Amazon prime logo
Buy new:
-13% $38.93
FREE delivery Monday, December 2
Ships from: Amazon
Sold by: Tiger Books LLC
$38.93 with 13 percent savings
List Price: $44.99
FREE Returns
FREE delivery Monday, December 2
Or fastest delivery Tuesday, November 26. Order within 5 hrs 54 mins
Only 1 left in stock - order soon.
$$38.93 () Includes selected options. Includes initial monthly payment and selected options. Details
Price
Subtotal
$$38.93
Subtotal
Initial payment breakdown
Shipping cost, delivery date, and order total (including tax) shown at checkout.
Ships from
Amazon
Ships from
Amazon
Returns
Returnable until Jan 31, 2025
Returnable until Jan 31, 2025
For the 2024 holiday season, eligible items purchased between November 1 and December 31, 2024 can be returned until January 31, 2025.
Read full return policy
Returns
Returnable until Jan 31, 2025
For the 2024 holiday season, eligible items purchased between November 1 and December 31, 2024 can be returned until January 31, 2025.
Read full return policy
Payment
Secure transaction
Your transaction is secure
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
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
$8.98
Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less See less
FREE delivery December 2 - 5. Details
Or fastest delivery November 29 - December 3. Details
In stock
$$38.93 () Includes selected options. Includes initial monthly payment and selected options. Details
Price
Subtotal
$$38.93
Subtotal
Initial payment breakdown
Shipping cost, delivery date, and order total (including tax) shown at checkout.
Access codes and supplements are not guaranteed with used items.
Ships from and sold by ThriftBooks-Atlanta.
Added to

Sorry, there was a problem.

There was an error retrieving your Wish Lists. Please try again.

Sorry, there was a problem.

List unavailable.
$38.93 with 13 percent savings
List Price: $44.99
FREE pickup Monday, December 2
Or fastest pickup Tuesday, November 26. Order within 5 hrs 54 mins

1.27 mi | ASHBURN 20147

How pickup works
Pick up from nearby pickup location
Step 1: Place Your Order
Select the “Pickup” option on the product page or during checkout.
Step 2: Receive Notification
Once your package is ready for pickup, you'll receive an email and app notification.
Step 3: Pick up
Bring your order ID or pickup code (if applicable) to your chosen pickup location to pick up your package.
Only 1 left in stock - order soon.
$$38.93 () Includes selected options. Includes initial monthly payment and selected options. Details
Price
Subtotal
$$38.93
Subtotal
Initial payment breakdown
Shipping cost, delivery date, and order total (including tax) shown at checkout.
Returns
Returnable until Jan 31, 2025
Returnable until Jan 31, 2025
For the 2024 holiday season, eligible items purchased between November 1 and December 31, 2024 can be returned until January 31, 2025.
Read full return policy
Returns
Returnable until Jan 31, 2025
For the 2024 holiday season, eligible items purchased between November 1 and December 31, 2024 can be returned until January 31, 2025.
Read full return policy
Payment
Secure transaction
Your transaction is secure
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
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
Kindle app logo image

Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required.

Read instantly on your browser with Kindle for Web.

Using your mobile phone camera - scan the code below and download the Kindle app.

QR code to download the Kindle App

Follow the author

Peter Harrington
Follow
Something went wrong. Please try your request again later.

Machine Learning in Action First Edition

by Peter Harrington (Author)
4.1 4.1 out of 5 stars 79 ratings
See all formats and editions
{"desktop_buybox_group_1":[{"displayPrice":"$38.93","priceAmount":38.93,"currencySymbol":"$","integerValue":"38","decimalSeparator":".","fractionalValue":"93","symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"vTGbDs1T8n1k0c%2FzdywEdNLtii%2FXuZIbtGBl%2BEDlX4BkpmgW7rFR%2B3Ve%2Bt%2B2vLnH0h5CRkWaTAEfrrxz%2FTYrPyWX6G%2FiAnfVfhFKducMEJ%2FfTCPiP24A0GrmmqL8cg8XyeAUETdL4pHIgUdQWthFQitP5dCfWDtVH8XxZx7dBSyFGRLC9X2LMuGtqSPywP3p","locale":"en-US","buyingOptionType":"NEW","aapiBuyingOptionIndex":0}, {"displayPrice":"$8.98","priceAmount":8.98,"currencySymbol":"$","integerValue":"8","decimalSeparator":".","fractionalValue":"98","symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"vTGbDs1T8n1k0c%2FzdywEdNLtii%2FXuZIbj46JfV1T8S4UXFCA6I8EL03MHFPsk2BUeCgPpH91P9hpIyQ0y9YFiGf1zZZ8KzjZ%2FVJdQZsAb4CV0V3zI5LKp2QASUaNMtX2D5wOtxwII%2Faodh1Wex7%2FFRKEB%2BfXCpgve3oiCegj6ws%3D","locale":"en-US","buyingOptionType":"USED","aapiBuyingOptionIndex":1}],"desktop_buybox_group_2":[{"displayPrice":"$38.93","priceAmount":38.93,"currencySymbol":"$","integerValue":"38","decimalSeparator":".","fractionalValue":"93","symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"vTGbDs1T8n1k0c%2FzdywEdNLtii%2FXuZIbtGBl%2BEDlX4BkpmgW7rFR%2B3Ve%2Bt%2B2vLnH0h5CRkWaTAEfrrxz%2FTYrPyWX6G%2FiAnfVfhFKducMEJ%2FfTCPiP24A0GrmmqL8cg8XyeAUETdL4pHIgUdQWthFQitP5dCfWDtVH8XxZx7dBSyFGRLC9X2LMuGtqSPywP3p","locale":"en-US","buyingOptionType":"PICKUP","aapiBuyingOptionIndex":2}]}

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
  1. PART 1 CLASSIFICATION
  2. Machine learning basics
  3. Classifying with k-Nearest Neighbors
  4. Splitting datasets one feature at a time: decision trees
  5. Classifying with probability theory: naïve Bayes
  6. Logistic regression
  7. Support vector machines
  8. Improving classification with the AdaBoost meta algorithm
  9. PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
  10. Predicting numeric values: regression
  11. Tree-based regression
  12. PART 3 UNSUPERVISED LEARNING
  13. Grouping unlabeled items using k-means clustering
  14. Association analysis with the Apriori algorithm
  15. Efficiently finding frequent itemsets with FP-growth
  16. PART 4 ADDITIONAL TOOLS
  17. Using principal component analysis to simplify data
  18. Simplifying data with the singular value decomposition
  19. Big data and MapReduce
Previous slide of product details
  1. ISBN-10
    9781617290183
  2. ISBN-13
    978-1617290183
  3. Edition
    First Edition
  4. Publisher
    Manning
  5. Publication date
    April 19, 2012
  6. Language
    English
  7. Dimensions
    7.38 x 0.8 x 9.25 inches
  8. Print length
    384 pages
  9. See all details
Next slide of product details

Frequently bought together

Machine Learning in Action
This item: Machine Learning in Action
$38.93
Get it as soon as Monday, Dec 2
Only 1 left in stock - order soon.
Sold by Tiger Books LLC and ships from Amazon Fulfillment.
+
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Syst
$49.99
Get it as soon as Wednesday, Nov 27
In Stock
Ships from and sold by Amazon.com.
+
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started
$28.66
Get it as soon as Wednesday, Nov 27
Only 6 left in stock (more on the way).
Ships from and sold by Amazon.com.
Total price: $00
To see our price, add these items to your cart.
Details
Added to Cart
spCSRF_Treatment
Some of these items ship sooner than the others.
Show details Hide details
Choose items to buy together.

Similar items that may deliver to you quickly

Page 1 of 1 Start overPage 1 of 1
Previous set of slides
  3. The Hundred-Page Machine Learning Book
    Andriy Burkov
     
    Paperback
    -28%$28.85
    List: $39.95
    Get it as soon as Wednesday, Nov 27
    FREE Shipping on orders over $35 shipped by Amazon
Next set of slides

Editorial Reviews

About the Author

Peter Harrington holds a Bachelors and a Masters Degrees in Electrical Engineering. He is a professional developer and data scientist. Peter holds five US patents and his work has been published in numerous academic journals.

Product details

  • ASIN ‏ : ‎ 1617290181
  • Publisher ‏ : ‎ Manning; First Edition (April 19, 2012)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 384 pages
  • ISBN-10 ‏ : ‎ 9781617290183
  • ISBN-13 ‏ : ‎ 978-1617290183
  • Item Weight ‏ : ‎ 1.42 pounds
  • Dimensions ‏ : ‎ 7.38 x 0.8 x 9.25 inches
  • Customer Reviews:
    4.1 4.1 out of 5 stars 79 ratings

About the author

Follow authors to get new release updates, plus improved recommendations.
Peter Harrington

Peter Harrington

Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Discover more of the author’s books, see similar authors, read book recommendations and more.

Customer reviews

4.1 out of 5 stars
79 global ratings

Customers say

Customers find the book a good introduction to machine learning and the main algorithms used in it. They say it provides a broad overview of techniques and is a valuable starting point for implementing them. However, some readers have reported that some of the code examples have errors, are repetitive, and confused. They also mention the editing can be poor. Opinions are mixed on readability, with some finding it well-written and easy to understand, while others say it reads like a rough draft.

AI-generated from the text of customer reviews

Select to learn more
IntroductionReadabilityCode qualityEditing
10 customers mention "Introduction"10 positive0 negative

Customers find the book's introduction helpful in learning the concepts of machine learning. They say it provides a fairly broad overview of the techniques and a valuable starting point for implementing them. Readers also mention that the code snippets are well-explained and short enough to be digestible. In addition, they say the book is good for code practice, has the best example code, and covers the most topics.

"...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..." Read more

"This book is a good introduction to the main algorithms used in machine learning: linear/logistics regression, kNN, decision and regression trees,..." Read more

"Great Book for machine learning and Python lover, I am sure it is a great book for people who are not familiar with matlab, this may be the fast way..." Read more

"...Learning In Action is the best because it is the most clear, has the best example code, and covers the most topics...." Read more

12 customers mention "Readability"8 positive4 negative

Customers have mixed opinions about the readability of the book. Some mention it's well-written and easy to understand, while others say it reads like a rough draft, is repetitive, and confused.

"...However, even though Python is an extremely readable language, machine learning algorithms are (generally) hard, and I found that it helped to..." Read more

"...It is easy to read and offers a good selection of algorithms...." Read more

"...It is not very difficult to read and practice, sometimes you may think of some better ideas from the book...." Read more

"...with other reviewers' complaints on the repetitiveness and poor flow of this book, but I want to point out some other concerns and..." Read more

4 customers mention "Code quality"0 positive4 negative

Customers find the code examples in the book have errors and are not written for legibility. They say it's repetitive, confusing, and often doesn't match up with the code and data sets to which it's referring.

"...a decent book, but IMO it has been edited poorly and the code has not been tested properly...." Read more

"...That being said, it's a horrendous piece of code clearly not written for legibility...." Read more

"...The text is repetitive, confused, and often doesn't match up with the code and data sets to which it refers...." Read more

"...As other reviewers have pointed out, some of the code examples have errors, which is frustrating...." Read more

3 customers mention "Editing"0 positive3 negative

Customers mention the editing is poor at times.

"...feel more like simplistic summaries around Python code and the editing can be poor at times repeating the same information between a main paragraph..." Read more

"...It's a decent book, but IMO it has been edited poorly and the code has not been tested properly...." Read more

"...Unfortunately, the book is poorly written and even more poorly edited; it reads like a very rough draft that was put once through a spell-checker..." Read more

Great Book: A few things might help
4 out of 5 stars
Great Book: A few things might help
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.
Hide
Thank you for your feedback
Sorry, there was an error
Sorry we couldn't load the review

Top reviews from the United States

Sujit Pal
5.0 out of 5 stars 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
 Report
enty
4.0 out of 5 stars 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
 Report
Chris in AU
5.0 out of 5 stars Great Book for machine learning and Python lover
Reviewed in the United States on March 5, 2015
Verified Purchase
Great Book for machine learning and Python lover, I am sure it is a great book for people who are not familiar with matlab, this may be the fast way to get yourself to do some really work rather than keep reading a lot of papers or pseudo codes.
It is not very difficult to read and practice, sometimes you may think of some better ideas from the book.
Also the idea of machine learning methods could also be helpful when you use other programming language to do some designs.
3 people found this helpful
Helpful
 Report
Z
5.0 out of 5 stars A must have for python data scientists.
Reviewed in the United States on January 28, 2015
Verified Purchase
I teach data science, and have read many many many books on the subject. Of them all, I feel Machine Learning In Action is the best because it is the most clear, has the best example code, and covers the most topics. I recommend this book for beginning data scientists, and advanced alike. The example code and data is also on github. If you are a python user, this book is a MUST.
3 people found this helpful
Helpful
 Report
Atreya
3.0 out of 5 stars 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
 Report

Top reviews from other countries

Translate all reviews to English
Frida
5.0 out of 5 stars Bueno
Reviewed in Mexico on March 7, 2021
Verified Purchase
No lo use como tal, pero me pareció buen libro
Report
Geevarghese samuel
5.0 out of 5 stars 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.
Report
NiCholasVance
2.0 out of 5 stars Two Stars
Reviewed in the United Kingdom on February 26, 2015
Verified Purchase
is it too much for 2 stars?
the codes have too much bugs
Report
Valerio Maggio
4.0 out of 5 stars Buon testo introduttivo
Reviewed in Italy on June 30, 2013
Verified Purchase
Testo introduttivo ai classici e maggiormente noti temi di Machine Leraning, declinati in una variante particolarmente orientata all'applicazione ( in Python ).

Corredato da numerosi esempi di codice più o meno "completi" fanno di questo testo una reference di alto livello per chi volesse avvicinarsi per la prima volta al mondo del Machine Learning utilizzando Python e le librerie scientifiche che mette a disposizione.

Consigliato!
Report
Dr. Chrilly Donninger
3.0 out of 5 stars 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.
Report