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Machine Learning with Python: Theory and Implementation 2023rd Edition

by Amin Zollanvari (Author)
4.2 out of 5 stars 6 ratings
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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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  1. ISBN-10
    3031333411
  2. ISBN-13
    978-3031333415
  3. Edition
    2023rd
  4. Publisher
    Springer
  5. Publication date
    July 12, 2023
  6. Language
    English
  7. Dimensions
    6.14 x 1 x 9.21 inches
  8. Print length
    469 pages
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Editorial Reviews

From the Back Cover

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.

About the Author

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.

Product details

  • Publisher ‏ : ‎ Springer; 2023rd edition (July 12, 2023)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 469 pages
  • ISBN-10 ‏ : ‎ 3031333411
  • ISBN-13 ‏ : ‎ 978-3031333415
  • Item Weight ‏ : ‎ 1.84 pounds
  • Dimensions ‏ : ‎ 6.14 x 1 x 9.21 inches
  • Customer Reviews:
    4.2 out of 5 stars 6 ratings

About the author

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Amin Zollanvari

Amin Zollanvari

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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, data analytics, programming, and 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.

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4.2 out of 5 stars
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Top reviews from the United States

  • Ebex
    5.0 out of 5 stars Great for beginners
    Reviewed in the United States on January 1, 2025
    Verified Purchase
    The book is wonderful. Very well explained with practical examples.
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  • Nicholas Alexander Ventura
    2.0 out of 5 stars Okay
    Reviewed in the United States on March 17, 2024
    Verified Purchase
    It focused on the image/pattern recognition too much and perhaps should be refocused on that. The presentation of each module was very good though.
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  • Luis Lpez Salgar
    5.0 out of 5 stars Pleasure to read
    Reviewed in the United States on April 24, 2024
    Verified Purchase
    Excelent book
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