000			03396cam a2200277 i 4500
003			PIMLIB
005			20240904145602.0
008			240831b en ||||| |||| 00| 0 eng d
020			_a9783031333422 _q(electronic bk.)
020			_a303133342X _q(electronic bk.)
020			_z3031333411
020			_a9783031333415
050		4	_aQ325.5 _b.Z864M _y2023
100			_aZollanvari, Amin _9100850
245	1	0	_aMachine learning with Python : _btheory and implementation / _cAmin Zollanvari
264		1	_aCham : _bSpringer, _c2023
300			_axvii, 452 p. : _b: ill. ; _c24 cm
504			_aIncludes bibliographical references and index
505	0		_aPreface -- 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
506			_aAvailable to OhioLINK libraries
520			_aThis 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	_aMachine learning _938969
650		0	_aPython (Computer program language) _959998
690			_945752 _a0033 ศิลปศาสตรบัณฑิต สาขาภาษาอังกฤษเพื่อการสื่อสารทางธุรกิจ CEB (ป.ตรี)
942			_2lcc _cBK _n0
999			_c1001242 _d1001242