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.
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Machine Learning in Action First Edition
by
Peter Harrington
(Author)
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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
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
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-109781617290183
- ISBN-13978-1617290183
- EditionFirst Edition
- PublisherManning
- Publication dateApril 19, 2012
- LanguageEnglish
- Dimensions7.38 x 0.8 x 9.25 inches
- Print length384 pages
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Top reviews from the United States
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Reviewed in the United States on April 27, 2022
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.
4.0 out of 5 stars
Great Book: A few things might help
Reviewed in the United States on April 27, 2022
This book is more details oriented and thorough than the other books I have read so far.Reviewed in the United States on April 27, 2022
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.
Images in this review
Reviewed in the United States on December 1, 2012
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.
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.
Reviewed in the United States on July 4, 2012
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.
Reviewed in the United States on January 19, 2013
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.
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.
Reviewed in the United States on June 17, 2012
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.
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.
Top reviews from other countries
Dr. Chrilly Donninger
3.0 out of 5 stars
Durchwachsene Einführung
Reviewed in Germany on June 24, 2012
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.
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.
Alan
4.0 out of 5 stars
Good Book for Those Starting Machine Learning.
Reviewed in India on November 6, 2018
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
4.0 out of 5 stars
Four Stars
Reviewed in India on October 2, 2017
Must read for all data scientists and analysts.
Amrit Dutta
3.0 out of 5 stars
Not a must read, good to read to scan the whole ML landscape.
Reviewed in India on June 9, 2018
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.
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.
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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
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.
The book is not for the impatient or faint hearts. The book shows actual implementation of various ML algorithms in Python using NumPy library.
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