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Learning scikit-learn: Machine Learning in Python Paperback – November 25, 2013
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About the Author
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 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.
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Top customer reviews
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.
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.
I'll recommend the book to people who can debug python codes by themselves and have some basic machine learning knowledge.
This book gives a short and brief introduction for scikit-learn. I did get some ideas about how to use scikit-learn to do some basic machine learning things. I regard this book as a more detailed document. It might be better if it can provide more mathematics intuition.
Quickly understand how scikit-learn works if you have already known some python and machine learning
Awesome IPython Notebook
Some codes cannot be compiled.
Some algorithms haven't been described clearly.
Some libraries such like Pandas hasn't been described clearly.
Lack of Math intuition.
What’s wrong with the title? Well, the book does not teach the scikit-learn package. It does show a very quick overview of some of its features.
What about the description? Well, the book does not introduce you to the concepts of machine learning. On the contrary, unless you have a decent background in ML, you will get lost. The authors don’t say a word about matplotlib and numpy either but believe me, if you can’t get through some code with confidence, you will end up looking at snippets of 10-15 lines without understanding what’s happening, Stack Overflow won’t save you.
The book itself is not that bad. I enjoyed the pages describing decision trees. I think this book, rather than being sold, should be used in the official scikit-learn webpage, as an overview.
Not worth the price. Not at all.
As usual, you can find more reviews on my personal blog: http://books.lostinmalloc.com. Feel free to pass by and share your thoughts!