Machine learning books
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Comprehensive Guide to Machine Learning Books
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
"Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron is a highly recommended resource for those looking to gain practical skills in machine learning. The book is divided into two main parts: fundamentals of machine learning and neural networks/deep learning. It provides a broad overview of machine learning concepts, challenges, and applications, and includes practical exercises at the end of each chapter to reinforce learning. The book also offers access to a GitHub repository with practice datasets and Jupyter notebooks, making it an excellent hands-on guide for both beginners and those with some experience in Python1.
Machine Learning - Algorithms, Models, and Applications
This volume presents innovative research works and their applications in real-world scenarios such as stock trading, medical and healthcare systems, and software automation. It is particularly useful for advanced graduate and doctoral students, researchers, and professionals working in the fields of machine learning, deep learning, and artificial intelligence. The book illustrates how machine learning and deep learning algorithms and models are designed, optimized, and deployed, making it a valuable resource for those looking to understand the practical applications of these technologies2.
An Introduction to Machine Learning
"An Introduction to Machine Learning" provides a high-level view of the basic concepts, models, and formulations behind machine learning problems. It discusses influence maximization and network diffusion, and analyzes selected algorithms developed to address these problems. This book is suitable for those seeking a foundational understanding of machine learning and its real-life applications3 5.
Machine Learning for Text
"Machine Learning for Text" by Charu C. Aggarwal is the first textbook to cover the intersections of data mining, language modeling, and deep learning in a holistic way. The book is organized into 14 chapters, each providing a comprehensive survey of text analytics from classical machine learning algorithms to sequence-centric text mining. It is an essential resource for those interested in natural language processing and text analytics4.
Learning and Soft Computing
"Learning and Soft Computing" by Vojislav Kecman is a comprehensive textbook that covers a broad range of topics including support vector machines, neural networks, and fuzzy logic models. The book is well-structured with practical examples, problems, and simulation experiments, making it an excellent resource for mastering the material. It also includes MATLAB-based software and PowerPoint slides, which are highly educational and user-friendly6.
Introduction to Machine Learning (Adaptive Computation and Machine Learning Series)
Ethem Alpaydin's "Introduction to Machine Learning" provides a comprehensive introduction to the theory of machine learning. The book is structured as a textbook and is suitable for graduate or undergraduate courses. It covers a wide range of topics and includes exercises that help readers consolidate their understanding. This book is an excellent resource for both students and professionals looking to deepen their knowledge of machine learning7.
A Brief Introduction to Machine Learning for Engineers
This monograph provides an introduction to key concepts, algorithms, and theoretical results in machine learning, focusing on probabilistic models for supervised and unsupervised learning problems. It is organized according to clearly defined categories and is suitable for researchers with a background in probability and linear algebra9.
C4.5: Programs for Machine Learning
J. Ross Quinlan's "C4.5: Programs for Machine Learning" is a definitive guide to decision tree algorithms, specifically the ID3 and C4.5 algorithms. This book is a valuable resource for researchers and students looking for an in-depth understanding of decision tree construction and its applications in machine learning10.
Conclusion
The books reviewed here offer a wide range of insights into machine learning, from practical hands-on guides to comprehensive theoretical frameworks. Whether you are a beginner or an advanced practitioner, these resources provide valuable knowledge and tools to enhance your understanding and application of machine learning techniques.
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Most relevant research papers on this topic
Book Review: Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd edition by Aurélien Géron
This book provides a comprehensive introduction to machine learning concepts and techniques, guiding readers through various applications and challenges.
Machine Learning - Algorithms, Models and Applications [Working Title]
Machine learning algorithms and models are rapidly transforming various industries, including stock trading, healthcare, and software automation.
Machine Learning for Text, by Charu C. Aggarwal, New York, Springer, 2018. ISBN 9783319735306. XXIII + 493 pages
Machine Learning for Text is a comprehensive survey of text analytics, integrating knowledge from machine learning, information retrieval, and natural language processing.
Learning and Soft Computing, Support Vector Machines, Neural Networks, and Fuzzy Logic Models, Vojislav Kecman; MIT Press, Cambridge, MA, 2001, ISBN 0-262-11255-8, 2001, pp 578
This book is a comprehensive introduction to learning from experimental data and soft computing, covering support vector machines, neural networks, and fuzzy logic systems.
Highly CitedEthem Alpaydin. Introduction to Machine Learning (Adaptive Computation and Machine Learning Series). The MIT Press, 2004, ISBN 0 262 01211 1
This book provides a comprehensive introduction to machine learning, enabling students, researchers, and application developers to implement key techniques as stand-alone software tools or analytical techniques for research.
A Brief Introduction to Machine Learning for Engineers
This monograph provides an introduction to key concepts, algorithms, and theoretical results in machine learning for engineers with a background in probability and linear algebra.
Highly Cited
PreprintBook Review: C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993
C4.5: Programs for Machine Learning provides a comprehensive description of J. Ross Quinlan's decision tree algorithms, including recent developments, making it a welcome addition to the library of researchers and students.
Highly Cited