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Machine Learning in Python: Essential Techniques for Predictive Analysis (Paperback)

Michael Bowles

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ISBN 10: 1118961749 / ISBN 13: 9781118961742
Editorial: John Wiley Sons Inc, United States, 2015
Nuevos Condición: New Encuadernación de tapa blanda
Librería: The Book Depository US (London, Reino Unido)

Librería en AbeBooks desde: 10 de septiembre de 2013

Cantidad: 10

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Descripción

Language: English . Brand New Book. Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions. Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language. Predict outcomes using linear and ensemble algorithm familiesBuild predictive models that solve a range of simple and complex problemsApply core machine learning algorithms using PythonUse sample code directly to build custom solutions Machine learning doesn t have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics. N° de ref. de la librería AAH9781118961742

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Detalles bibliográficos

Título: Machine Learning in Python: Essential ...

Editorial: John Wiley Sons Inc, United States

Año de publicación: 2015

Encuadernación: Paperback

Condición del libro:New

Edición: 1. Auflage.

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Sinopsis:

Learn a simpler and more effective way to analyze data and predict outcomes with Python

Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions.

Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language.

  • Predict outcomes using linear and ensemble algorithm families
  • Build predictive models that solve a range of simple and complex problems
  • Apply core machine learning algorithms using Python
  • Use sample code directly to build custom solutions

Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.

From the Back Cover:

SIMPLE, EFFECTIVE WAY TO ANALYZE DATA AND PREDICT OUTCOMES WITH PYTHON

Machine learning focuses on prediction—using what you know to predict what you would like to know based on historical relationships between the two. At its core, it's a mathematical/algorithm-based technology that, until recently, required a deep understanding of math and statistical concepts, and fluency in R and other specialized languages. Machine Learning in Python simplifies machine learning for a broader audience and wider application by focusing on two algorithm families that effectively predict outcomes, and by showing you how to apply them using the popular and accessible Python programming language.

Author Michael Bowles draws from years of machine learning expertise to walk you through the design, construction, and implementation of your own machine learning solutions. The algorithms are explained in simple terms with no complex math, and sample code is provided to help you get started right away. You'll delve deep into the mechanisms behind the constructs, and learn how to select and apply the algorithm that will best solve the problem at hand, whether simple or complex. Detailed examples illustrate the machinery with specific, hackable code, and descriptive coverage of linear regression and ensemble methods helps you understand the fundamental processes at work in machine learning. The methods are effective and well tested, and the results speak for themselves.

Designed specifically for those without a specialized math or statistics background, Machine Learning in Python shows you how to:

  • Select the right algorithm for the job
  • Learn the mechanisms and prepare the data
  • Master core Python machine learning packages
  • Build versatile predictive models that work
  • Apply trained models in practice for various uses
  • Measure model performance for better QC and application
  • Use provided sample code to design and build your own model

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