Machine Learning with Python

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.

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 Supplemented by exercises, serves as a self-sufficient book for readers with no Python programming experience Request lecturer material: sn.pub/lecturer-material Anyone interested in unrestricted lecturer materials can access the files at https://drive.google.com/drive/folders /1vgst3Y1hjgNLgXv2lBq2Wf8cJBmwE_IK?usp=sharing

Autorentext

Amin Zollanvari is an Associate Professor of Electrical and Computer Engineering and the Head of Data Science Laboratory at Nazarbayev University. He received his B.Sc. and M.Sc. degrees in electrical engineering from Shiraz University, Iran, in 2003 and 2006, respectively, and a Ph.D. in electrical engineering from Texas A&M University, in 2010. He held a postdoctoral position at Harvard Medical School and Brigham and Women's Hospital, Boston MA (2010-2012), and later joined the Department of Statistics at Texas A&M University as an Assistant Research Scientist (2012-2014). He has taught a number of courses on machine learning, programming, and statistical signal processing both at graduate and undergraduate level and has authored over 80 research papers in prestigious journals and international conferences on fundamental and practical machine learning and pattern recognition. He is currently an IEEE Senior member and has served as an Associate Editor of IEEE Access since 2018.

Klappentext

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.

Inhalt
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.