Mikel Galar's Books

Large-Scale Data Analytics with Python and Spark

A Hands-on Guide to Implementing Machine Learning Solutions

By: Isaac Triguero,Mikel Galar

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Large-Scale Data Analytics with Python and Spark

A Hands-on Guide to Implementing Machine Learning Solutions

By: Isaac Triguero,Mikel Galar

Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.

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Page Count

396

Publisher

Cambridge University Press

Published Date

ISBN 10

1009318233

ISBN 13

9781009318235

Learning from Imbalanced Data Sets

By: Alberto Fernández,Salvador García,Mikel Galar,Ronaldo C. Prati,Bartosz Krawczyk,Francisco Herrera

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Learning from Imbalanced Data Sets

By: Alberto Fernández,Salvador García,Mikel Galar,Ronaldo C. Prati,Bartosz Krawczyk,Francisco Herrera

This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.

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Page Count

385

Publisher

Springer

Published Date

ISBN 10

3319980742

ISBN 13

9783319980744

Cat‡logo flor’stico de Navarra

By: Mikel Lorda LÃ³pez

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Cat‡logo flor’stico de Navarra

By: Mikel Lorda LÃ³pez

Primera obra moderna sobre las plantas vasculares de la Comunidad Foral de Navarra. NO ES UNA GUÍA FOTOGRÁFICA Viene estructurado por orden alfabético de familias. Se enumeran un total de 3073 taxones, de los que 2796 se son propios de la flora navarra actual. De cada uno se da su estatus de presencia; origen y ecología; hábitats de importancia comunitaria en los que se puede encontrar -cuando se trata de un taxon característico-; distribución general, en comarcas geográficas y biogeográficas (regiones Alpina, Atlántica y Mediterránea), en este último caso asignando un índice de frecuencia/abundancia; estatus legal de conservación cuando existe; observaciones de índole taxonómica o nomenclatural, errores de citas, localidades relevantes, presencia en el Catálogo español de especies exóticas invasoras, etc. Tiene un capítulo sintético sobre diversos aspectos de la flora navarra, sus grupos corológicos, endemismos, grado de abundancia/rareza por sectores corológicos y las plantas protegidas o catalogadas.

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