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Machine Learning,New
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As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, examplebased approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with wellchosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and stateoftheart topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: What is the main focus of 'Machine Learning: The Art and Science of Algorithms that Make Sense of Data'? A: The book focuses on providing a comprehensive understanding of machine learning, emphasizing its rich variety of algorithms and the unifying principles behind them.
- Q: Who is the author of this machine learning textbook? A: The author of the book is Peter Flach, who is known for his clear and example-based approach to explaining complex machine learning concepts.
- Q: What is the publication date of this book? A: The book was published on November 12, 2012.
- Q: How many pages does this book have? A: The book contains 409 pages, making it a thorough resource for learning machine learning.
- Q: Is this book suitable for beginners in machine learning? A: Yes, the book is designed as an introductory textbook, making it suitable for beginners while also providing depth for more advanced readers.
- Q: What topics are covered in this machine learning book? A: The book covers a wide array of topics including logical, geometric, statistical models, matrix factorization, ROC analysis, and the importance of features in algorithms.
- Q: What is the binding type of this book? A: The book is available in paperback binding, which is common for academic textbooks.
- Q: Are there any case studies included in the book? A: Yes, the book includes case studies of increasing complexity, along with examples and illustrations that aid in understanding machine learning concepts.
- Q: Does this book provide summaries or revision pointers? A: Yes, the book includes summaries of relevant background material and provides pointers for revision, ensuring readers can reinforce their understanding.
- Q: What category does this book belong to? A: This book is categorized under Computer Vision & Pattern Recognition, focusing on algorithms that make sense of data.