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Learning scikit-learn: Machine Learning in Python von [Garreta, Raúl, Moncecchi, Guillermo]
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Learning scikit-learn: Machine Learning in Python Kindle Edition

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In Detail

Machine learning, the art of creating applications that learn from experience and data, has been around for many years. However, in the era of “big data”, huge amounts of information is being generated. This makes machine learning an unavoidable source of new data-based approximations for problem solving.With Learning scikit-learn: Machine Learning in Python, you will learn to incorporate machine learning in your applications. The book combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. Ranging from handwritten digit recognition to document classification, examples are solved step by step using Scikit-learn and Python. The book starts with a brief introduction to the core concepts of machine learning with a simple example. Then, using real-world applications and advanced features, it takes a deep dive into the various machine learning techniques.You will learn to evaluate your results and apply advanced techniques for preprocessing data. You will also be able to select the best set of features and the best methods for each problem. With Learning scikit-learn: Machine Learning in Python you will learn how to use the Python programming language and the scikit-learn library to build applications that learn from experience, applying the main concepts and techniques of machine learning.


The book adopts a tutorial-based approach to introduce the user to Scikit-learn.

Who this book is for

If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.

Über den Autor und weitere Mitwirkende

Raúl Garreta

Raúl Garreta is a Computer Engineer with much experience in the theory and application of Artificial Intelligence (AI), where he specialized in Machine Learning and Natural Language Processing (NLP).

He has an entrepreneur profile with much interest in the application of science, technology, and innovation to the Internet industry and startups. He has worked in many software companies, handling everything from video games to implantable medical devices.

In 2009, he co-founded Tryolabs with the objective to apply AI to the development of intelligent software products, where he performs as the CTO and Product Manager of the company. Besides the application of Machine Learning and NLP, Tryolabs' expertise lies in the Python programming language and has been catering to many clients in Silicon Valley. Raul has also worked in the development of the Python community in Uruguay, co-organizing local PyDay and PyCon conferences.

He is also an assistant professor at the Computer Science Institute of Universidad de la República in Uruguay since 2007, where he has been working on the courses of Machine Learning, NLP, as well as Automata Theory and Formal Languages. Besides this, he is finishing his Masters degree in Machine Learning and NLP. He is also very interested in the research and application of Robotics, Quantum Computing, and Cognitive Modeling. Not only is he a technology enthusiast and science fiction lover (geek) but also a big fan of arts, such as cinema, photography, and painting.

Guillermo Moncecchi

Guillermo Moncecchi is a Natural Language Processing researcher at the Universidad de la República of Uruguay. He received a PhD in Informatics from the Universidad de la República, Uruguay and a Ph.D in Language Sciences from the Université Paris Ouest, France. He has participated in several international projects on NLP. He has almost 15 years of teaching experience on Automata Theory, Natural Language Processing, and Machine Learning.

He also works as Head Developer at the Montevideo Council and has lead the development of several public services for the council, particularly in the Geographical Information Systems area. He is one of the Montevideo Open Data movement leaders, promoting the publication and exploitation of the city's data.


  • Format: Kindle Edition
  • Dateigröße: 1536 KB
  • Seitenzahl der Print-Ausgabe: 118 Seiten
  • Verlag: Packt Publishing (25. November 2013)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • ASIN: B00GX67UEY
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Word Wise: Nicht aktiviert
  • Screenreader: Unterstützt
  • Verbesserter Schriftsatz: Aktiviert
  • Durchschnittliche Kundenbewertung: 2.0 von 5 Sternen 2 Kundenrezensionen
  • Amazon Bestseller-Rang: #524.918 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)

  •  Ist der Verkauf dieses Produkts für Sie nicht akzeptabel?


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Format: Kindle Edition
I am a software developer and father of 2 small boys. Add these together and I don't generally have a lot of time for reading and because of this that I tend to love Packt's [Instant] series. These short introductions give me an idea if I want to invest more of my time on a subject.
I was already passingly acquainted with scikit-learn so this subject wasn't entirely new to me but, in this case, I can see that it might be a little harder for those coming completely blind to the subject.
One of the great things about the book is the inclusion of code in the form of IPython notebooks making it fairly easy to get started tweaking and testing. It is well written and fairly easy to follow.
Well, easy to follow if you already have a grasp on the maths and debugging of Python programmes. More in depth explanations would of course be great but this is an [Instant] book and you can't really expect and in depth coverage from it you just get your feet wet.
I would have given the book 4 stars but unfortunately not all the code can be interpreted as is and that might be frustrating to some.
Kommentar Eine Person fand diese Informationen hilfreich. War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
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Format: Taschenbuch Verifizierter Kauf
Dieses Buch ist eines der wenigen, die ich wieder postwendend an Amazon zurückgeschickt habe. Umfang und inhalt sind eher mit einem Web-Tutorial vergleichbar und die Präsentation erschien mir eher lieblos.
Kommentar 2 Personen fanden diese Informationen hilfreich. War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback.
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Die hilfreichsten Kundenrezensionen auf (beta) (Kann Kundenrezensionen aus dem "Early Reviewer Rewards"-Programm beinhalten) 3.2 von 5 Sternen 16 Rezensionen
91 von 95 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Badly written, adds little to docmentation 4. März 2014
Von Andreas Mueller - Veröffentlicht auf
Format: Taschenbuch
This books is pretty badly written and contains many factual errors. I was reviewer for the book before I quit the project, as my comments where not addressed (I am one of the core developers of scikit-learn) and the quality of code, content and language was very low.

I think you get much better information if you have a look at the online tutorials on scikit-learn, of which there are many. There is definitely room for a good scikit-learn book for practical machine learning, but this is not it.
19 von 21 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Explains little, erroneous code 4. Februar 2014
Von Anne Markis - Veröffentlicht auf
Format: Kindle Edition Verifizierter Kauf
This book was a disappointment to me. I'm going to say that 80% of the code examples didn't compile if typed directly from the text - usually due to something dumb like an unmentioned import but still a bummer to spend time trying to figure it out. As far as the content, I learned very little: it seemed like it was merely an elongated version of their documentation online, only with more details and less meaning.
2 von 3 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Great book if you're highly motivated to learn ML techniques! 11. August 2014
Von Amazon Customer - Veröffentlicht auf
Format: Kindle Edition Verifizierter Kauf
I'm giving this book 5 stars because I found it useful in getting my feet wet with machine learning (ML). If you want to see what kind of ML tools are in use now (or at least recently) using free software and you know how to code in Python and you know enough about debugging computer programs to find all the errors in the author's code then maybe this book is for you too.
free software (if you can get it to run on your computer...not very easy)
book is very affordable
software is devilishly hard to get working (I had to get a commercial version to make it work on Win7X64)
authors code is loaded with errors, you MUST find an errata list and keep it with you as you read (if you run the code)
even the errata list from the publisher is incomplete I found several additional errors and I ran every problem
authors explanation of ML and software is mushy, feels like he's giving you a quick demo in his office on a few interesting ML problems
Most of these follow-along-with-me books have these same problems, I've read many, so I was prepared for frustration but if you're not a coder or if you're new to installing software I'd suggest reading the scikit example pages that you can find on the interwebs.
I really, really want to use these methods to help pick winning stocks so I'm highly motivated to hammer through the problems and make it work; great starting point for me. Your mileage may vary.
1 von 2 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen Average reading 18. Mai 2014
Von Amazon Customer - Veröffentlicht auf
Format: Kindle Edition Verifizierter Kauf
You will need some background in computer science or machine learning. It is a good book and it was kind of hard for me because I don't have any background in machine learning. I had to reread it couple times. I gave it 3 stars.
2 von 4 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Great book for python and machine learning 29. Dezember 2013
Von Noam Peled - Veröffentlicht auf
Format: Kindle Edition Verifizierter Kauf
Great book!
The book is very coherence and deductive.
Also, the ipython notebooks are awesome!
Very recommended even as a review on machine learning
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