[pyar] [ANN] Libro: Learning scikit-learn: Machine Learning in Python

[Elias]

Hace poquito acaba de publicarse el libro:
*Learning scikit-learn: Machine Learning in Python*
ya está disponible en Amazon: http://amzn.com/1783281936
Autores: Raúl Garreta, Guillermo Moncecchi

Ambos de la comunidad python de Uruguay :D,

Saludos, Elías

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Experience the benefits of machine learning techniques by applying them to
real-world problems using Python and the open source scikit-learn library

*Overview*

   - Use Python and scikit-learn to create intelligent applications
   - Apply regression techniques to predict future behaviour and learn to
   cluster items in groups by their similarities
   - Make use of classification techniques to perform image recognition and
   document classification

*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.

*What you will learn from this book*

   - Set up scikit-learn inside your Python environment
   - Classify objects (from documents to human faces and flower species)
   based on some of their features, using a variety of methods from Support
   Vector Machines to Naïve Bayes
   - Use Decision Trees to explain the main causes of certain phenomenon
   such as the Titanic passengers’ survival
   - Predict house prices using regression techniques
   - Display and analyse groups in your data using dimensionality reduction
   - Make use of different tools to preprocess, extract, and select the
   learning features
   - Select the best parameters for your models using model selection
   - Improve the way you build your models using parallelization techniques

*Approach*

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

*Who this book is written 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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