Title
Machine Learning
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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: How many pages does this book have? A: This book has four hundred nine pages. It provides extensive coverage of machine learning concepts with numerous examples and illustrations.
- Q: What is the binding type of this book? A: The binding type is paperback. This makes it lightweight and easy to handle, ideal for students and professionals alike.
- Q: What are the dimensions of this book? A: The book measures seven point four four inches in length, zero point nine eight inches in width, and nine point six nine inches in height. These dimensions make it portable for on-the-go learning.
- Q: Who is the author of this book? A: The author is Peter Flach. He is known for his clear and example-based approach to explaining complex machine learning topics.
- Q: What topics does this book cover? A: This book covers a wide range of topics including logical, geometric, and statistical models. It also includes advanced topics like matrix factorization and ROC analysis.
- Q: Is this book suitable for beginners? A: Yes, the book is suitable for beginners. Flach's approach starts with basic concepts and gradually introduces more complex ideas.
- Q: Can I use this book for self-study? A: Yes, this book is ideal for self-study. It includes case studies, examples, and summaries that help reinforce the learning material.
- Q: Is this book appropriate for a college course? A: Yes, it is appropriate for a college course. It provides a comprehensive introduction to machine learning that aligns well with academic curricula.
- Q: How should I care for this book? A: To care for this book, keep it away from moisture and extreme temperatures. Store it in a cool, dry place to preserve its quality.
- Q: Is this book safe for young readers? A: Yes, the content is appropriate for young readers interested in technology. However, some concepts may require adult guidance for full understanding.
- Q: What if I receive a damaged copy of the book? A: If you receive a damaged copy, contact the seller for a return or exchange. Most sellers have policies in place for damaged items.
- Q: Are there any online resources that complement this book? A: Yes, there are online resources available that complement this book. Many authors provide additional materials, exercises, and forums for discussion.
- Q: Is there a warranty on this book? A: Typically, there is no warranty on books. However, check with the retailer for their specific return and exchange policies.
- Q: Can I find this book in libraries? A: Yes, many libraries carry this book. It is a popular choice for those studying machine learning.
- Q: Is there an eBook version available? A: Yes, an eBook version is often available. Check online retailers for digital formats that can be read on various devices.