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020 _a9783031333422
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024 7 _a10.1007/978-3-031-33342-2
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100 1 _aZollanvari, Amin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMachine Learning with Python
_h[electronic resource] :
_bTheory and Implementation /
_cby Amin Zollanvari.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXVII, 452 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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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.
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.
650 0 _aPython (Computer program language).
650 0 _aArtificial intelligence
_xData processing.
650 0 _aPattern recognition systems.
650 0 _aArtificial intelligence.
650 1 4 _aMachine Learning.
650 2 4 _aPython.
650 2 4 _aData Science.
650 2 4 _aAutomated Pattern Recognition.
650 2 4 _aArtificial Intelligence.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031333415
776 0 8 _iPrinted edition:
_z9783031333439
776 0 8 _iPrinted edition:
_z9783031333446
856 4 0 _uhttps://doi.org/10.1007/978-3-031-33342-2
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c179010
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