Learning scikit-learn: Machine Learning in Python
By Raúl Garreta, Guillermo Moncecchi
Publisher: Packt Publishing
Final Release Date: November 2013
Pages: 100

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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oreillyLearning scikit-learn: Machine Learning in Python
 
2.0

(based on 2 reviews)

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(1 of 1 customers found this review helpful)

 
3.0

Short & (kinda) Sweet

By Marc

from Vienna, Austria

About Me Developer

Verified Reviewer

Pros

  • Easy to understand
  • Helpful examples
  • Well-written

Cons

  • Too many errors

Best Uses

  • Novice

Comments about oreilly Learning scikit-learn: Machine Learning in Python:

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 4 customers found this review helpful)

 
1.0

Some Nuggetes, Poorly put Together

By edgenox

from Nairobi Kenya

About Me Designer, Developer

Verified Buyer

Pros

  • Helpful examples

Cons

  • Not comprehensive enough
  • Too many errors

Best Uses

  • Novice

Comments about oreilly Learning scikit-learn: Machine Learning in Python:

While you will find some interesting methods to deal with data, the book is made almost impossible due to typographical errors and missing dataset, that are continually referenced in the text.

Here are some examples:

Page 9
iris.target.target_names
No such module: iris.target.target_names

Page 13
from sklearn.linear_modelsklearn._model import SGDClassifier
no such module: linear_modelsklearn._model

Page 14
Xs -- Variable does not exist

Page 19
('scaler', StandardScaler()),

StandardScaler never imported directly should be preprocessing.StandardScaler()

The handing of cross validation is so slim that any one with no knowledge of what it is simply has
to take your work for it.

Page 33
eval_faces = [np.reshape(a, (64, 64)) for a in X_eval]
X_eval is never declared

Page 39
for line in open('stopwords_en.txt', 'r').readlines():
The file stopwords_en.txt does not exist

And on and on ...

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