國立高雄大學圖資館 |

Machine learning with Pythontheory and implementation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning with Pythonby Amin Zollanvari.
其他題名:	theory and implementation /
作者:	Zollanvari, Amin.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	xvii, 452 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-3-031-33342-2
ISBN:	9783031333422$q(electronic bk.)

Machine learning with Pythontheory and implementation /
Zollanvari, Amin.

Machine learning with Pythontheory and implementation /[electronic resource] :by Amin Zollanvari. - Cham :Springer International Publishing :2023. - xvii, 452 p. :ill., digital ;24 cm.

Preface -- About This Book -- 1. Introduction -- 2. Getting Started with Python -- 3. Three Fundamental Python Packages -- 4. Supervised Learning in Practice: The First Application Using Scikit-Learn. - 5. K-Nearest Neighbors -- 6. Linear Models -- 7. Decision Trees -- 8. Ensemble Learning -- 9. Model Evaluation and Selection -- 10. Feature Selection -- 11. Assembling Various Learning Stages -- 12. Clustering -- 13. Deep Learning with Keras-TensorFlow. - 14. Convolutional Neural Networks -- 15. Recurrent Neural Networks -- References.

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.

ISBN: 9783031333422$q(electronic bk.)

Standard No.: 10.1007/978-3-031-33342-2doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning with Pythontheory and implementation /
LDR:03316nmm a2200325 a 4500 001 648323
003 DE-He213
005 20230711140943.0
006 m d
007 cr nn 008maaau
008 240116s2023 sz s 0 eng d
020 $a 9783031333422$q(electronic bk.)
020 $a 9783031333415$q(paper)
024 7 $a 10.1007/978-3-031-33342-2 $2 doi
035 $a 978-3-031-33342-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .Z86 2023
100 1 $a Zollanvari, Amin. $3 958068
245 1 0 $a Machine learning with Python $h [electronic resource] : $b theory and implementation / $c by Amin Zollanvari.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xvii, 452 p. : $b ill., digital ; $c 24 cm.
505 0 $a Preface -- About This Book -- 1. Introduction -- 2. Getting Started with Python -- 3. Three Fundamental Python Packages -- 4. Supervised Learning in Practice: The First Application Using Scikit-Learn. - 5. K-Nearest Neighbors -- 6. Linear Models -- 7. Decision Trees -- 8. Ensemble Learning -- 9. Model Evaluation and Selection -- 10. Feature Selection -- 11. Assembling Various Learning Stages -- 12. Clustering -- 13. Deep Learning with Keras-TensorFlow. - 14. Convolutional Neural Networks -- 15. Recurrent Neural Networks -- References.
520 $a 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.
650 0 $a Machine learning. $3 188639
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Python. $3 763308
650 2 4 $a Data Science. $3 913495
650 2 4 $a Automated Pattern Recognition. $3 913155
650 2 4 $a Artificial Intelligence. $3 212515
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-33342-2
950 $a Computer Science (SpringerNature-11645)