| 书目名称 | Machine Learning with Python | | 副标题 | Theory and Implement | | 编辑 | Amin Zollanvari | | 视频video | | | 概述 | This textbook focuses on the most essential elements and practically useful techniques in Machine Learning.Strikes a balance between the theory of Machine Learning and implementation in Python.Supplem | | 图书封面 |  | | 描述 | 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 theoret | | 出版日期 | Textbook 2023 | | 关键词 | Keras-TensorFlow; Clustering; Convolutional Neural Networks; Decision Trees; Deep Learning; Ensemble Lear | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-33342-2 | | isbn_softcover | 978-3-031-33344-6 | | isbn_ebook | 978-3-031-33342-2 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 16:34:17
