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Learning scikit-learn: Machine Learning in Python [Kindle Edition]

Raúl Garreta , Guillermo Moncecchi
3.5 out of 5 stars  See all reviews (2 customer reviews)

Print List Price: £18.99
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Book Description

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.

Approach

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.


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Product Description

About the Author

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.


Product details

  • Format: Kindle Edition
  • File Size: 947 KB
  • Print Length: 118 pages
  • Publisher: Packt Publishing (25 Nov. 2013)
  • Sold by: Amazon Media EU S.ą r.l.
  • Language: English
  • ASIN: B00GX67UEY
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Enhanced Typesetting: Not Enabled
  • Average Customer Review: 3.5 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: #407,125 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most Helpful Customer Reviews
1 of 1 people found the following review helpful
Format:Kindle Edition
When you browse Amazon’s catalog and read “…the book starts with a brief introduction to the core concepts of machine learning. Then, using real-world applications and advanced features, it takes a deep dive into the various machine learning techniques. You will learn to evaluate results and apply advanced techniques.” you expect the book to deliver. It does not, which makes it a big disappointment.

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!
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4 of 5 people found the following review helpful
By MaxB
Format:Paperback|Verified Purchase
This is a tiny book, at less than one hundred pages it can easily be read and the programming examples tried and understood well by those with the necessary background(Machine learning or statistics/CS) over a weekend. However for those without any machine learning background looking to enter or understand the field this will not be the correct book for you.

As someone already familiar with machine learning (ml) through both other books and Coursera courses I found the focus on implementation and programming and complete lack of maths or theory helpful as I am already quite familiar with the background and maths. My goal in reading this book was only to quickly get up to speed with the Scikit package.

Before reading this book I had significant R, some MATLAB ml experience and with some python experience outside of Scikit. However after reading this book and spending the last 3 days working the examples and a few Kaggle competitions I can say with certainty that Scikit is now my preferred choice for ml and I will be investing significantly more time in it going forwards.

I will be involved in implementing an ml project in work during the next few months and being able to digest this book over a weekend has been tremendously helpful.

So to summarise

This book is great if:
A relatively experienced programmer preferably in Python but it doesn't matter too much. (Python for Data Analysis might be a good read before for those unfamiliar)

You are more than familiar with machine learning, know your supervised vs unsupervised learning algorithms, know your different models, understand what is going on in the background etc...

Want to quickly get a handle on using Scikit with minimal extra material

This book is not for you if:
You want to understand or get into either python or machine learning

You want explanation or understanding of many of the other packages used outside Scikit.
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Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com: 3.3 out of 5 stars  15 reviews
65 of 68 people found the following review helpful
2.0 out of 5 stars Badly written, adds little to docmentation 4 Mar. 2014
By Andreas Mueller - Published on Amazon.com
Format:Paperback
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.
13 of 13 people found the following review helpful
3.0 out of 5 stars Quickly understand how scikit-learn works if you have already know some python and machine learning 11 Feb. 2014
By Willy - Published on Amazon.com
Format:Kindle Edition
Short Answer
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.

Pros
Quickly understand how scikit-learn works if you have already known some python and machine learning
Awesome IPython Notebook

Cons:
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.
14 of 16 people found the following review helpful
2.0 out of 5 stars Explains little, erroneous code 4 Feb. 2014
By Anne Markis - Published on Amazon.com
Format:Kindle Edition|Verified Purchase
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 of 2 people found the following review helpful
3.0 out of 5 stars Short & (kinda) Sweet 17 Feb. 2014
By Marc-Anthony Taylor - Published on Amazon.com
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.
2 of 2 people found the following review helpful
2.0 out of 5 stars Save your money, download the scikit-learn user guide instead 10 May 2015
By Cameron Cairns - Published on Amazon.com
Format:Kindle Edition
I just want to reiterate some of the previous comments. If you want to learn how to use scikit-learn skip this book and download the free scikit-learn user guide: (http://scikit-learn.org/stable/user_guide.html) and work through the first 5-6 chapters.

The documentation is more comprehensive, has more examples, and (in my opinion) has better written code and clearer explanations of the algorithms. In short the book under review adds precious little that can't be gained from the developer's documentation. Pakt publishing (the publishers of this book) has a reputation for churning out poorly conceived and hastily written software and tech books and unfortunately this is another example.
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