Machine Learning with Python : Theory and Implementation / by Amin Zollanvari.
Material type: TextEdition: 1st ed. 2023Description: (XVII, 452 p.) 24cmISBN: 9783031333422; 9783031333415; 9783031333439; 9783031333446Subject(s): Machine learning | Python (Computer program language) | Artificial intelligence-Data processing | Pattern recognition systems | Artificial intelligenceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.31 LOC classification: Q325.5-.7Online resources: Full text available from Springer Nature - Springer Computer Science eBooks 2023 English InternationalItem type | Current library | Call number | Status | Date due | Barcode | Item holds |
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Books | Namal Library Computer Science | 006.31 ZOL-M 2023 14282 (Browse shelf (Opens below)) | Available | 0014282 |
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006.31 SOM-M 2011 606 Machine learning with SVM and other kernel methods / | 006.31 TAU-G 2023 14210 Generative AI : How ChatGPT and Other AI Tools Will Revolutionize Business / | 006.31 THE-M 2021 12213 Machine learning for absolute beginners / | 006.31 ZOL-M 2023 14282 Machine Learning with Python : Theory and Implementation / | 006.312 ERL-B 2018 9747 Big data fundamentals : | 006.312 ERL-B 2018 9851 Big data fundamentals : | 006.312 HAI-G 2016 9734 Getting started with data science : |
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.
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