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Machine Learning with Python

Amin Zollanvari (Gebundene Ausgabe, Englisch)

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Beschreibung
This book is meant as a textbook for undergraduate and graduate students who are willing to understand essential elements of machine learning from both a theoretical and a practical perspective. The choice of the topics in the book is made based on one criterion: whether the practical utility of a certain method justifies its theoretical elaboration for students with a typical mathematical background in engineering and other quantitative fields. As a result, not only does the book contain practically useful techniques, it also presents them in a mathematical language that is accessible to both graduate and advanced undergraduate students. The textbook covers a range of topics including nearest neighbors, linear models, decision trees, ensemble learning, model evaluation and selection, dimensionality reduction, assembling various learning stages, clustering, and deep learning along with an introduction to fundamental Python packages for data science and machine learning such as NumPy, Pandas, Matplotlib, Scikit-Learn, XGBoost, and Keras with TensorFlow backend. Given the current dominant role of the Python programming language for machine learning, the book complements the theoretical presentation of each technique by its Python implementation. In this regard, two chapters are devoted to cover necessary Python programming skills. This feature makes the book self-sufficient for students with different programming backgrounds and is in sharp contrast with other books in the field that assume readers have prior Python programming experience. As such, the systematic structure of the book, along with the many examples and exercises presented, will help the readers to better grasp the content and be equipped with the practical skills required in day-to-day machine learning applications.
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Technische Daten


Erscheinungsdatum
12.07.2023
Sprache
Englisch
EAN
9783031333415
Herausgeber
Springer International Publishing
Sonderedition
Nein
Autor
Amin Zollanvari
Seitenanzahl
452
Einbandart
Gebundene Ausgabe
Buch Untertitel
Theory and Implementation
Autorenporträt
Amin Zollanvari is an Associate Professor of Electrical and Computer Engineering and the Head of Data Science Laboratory at Nazarbayev University. He received his B.Sc. and M.Sc. degrees in electrical engineering from Shiraz University, Iran, in 2003 and 2006, respectively, and a Ph.D. in electrical engineering from Texas A&M University, in 2010. He held a postdoctoral position at Harvard Medical School and Brigham and Women’s Hospital, Boston MA (2010-2012), and later joined the Department of Statistics at Texas A&M University as an Assistant Research Scientist (2012-2014). He has taught a number of courses on machine learning, programming, and statistical signal processing both at graduate and undergraduate level and has authored over 80 research papers in prestigious journals and international conferences on fundamental and practical machine learning and pattern recognition. He is currently an IEEE Senior member and has served as an Associate Editor of IEEE Access since 2018.
Schlagwörter
Keras-TensorFlow, Clustering, Convolutional Neural Networks, Decision Trees, Deep Learning, Ensemble Learning, Feature Selection, Machine Learning, Matplotlib, NumPy, Pandas, Pattern Recognition, Python Implementation, Recurrent Neural Networks, Scikit-Learn, Supervised Learning, Unsupervised Learning, XGBoost
Thema-Inhalt
UYQM - Maschinelles Lernen UMX - Programmier- und Skriptsprachen, allgemein UN - Datenbanken UYQP - Mustererkennung UYQ - Künstliche Intelligenz
Höhe
235 mm
Breite
15.5 cm

Transparenz & Sicherheit

Hersteller: Springer, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com, Springer Nature Customer Service Center GmbH

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