- Hardcover: 594 pages
- Publisher: Cambridge University Press; 2 edition (March 12, 2020)
- Language: English
- ISBN-10: 1108480721
- ISBN-13: 978-1108480727
- Product Dimensions: 7.2 x 1.1 x 10 inches
- Shipping Weight: 2.8 pounds (View shipping rates and policies)
- Customer Reviews: 1 customer rating
- Amazon Best Sellers Rank: #68,624 in Books (See Top 100 in Books)
Machine Learning Refined: Foundations, Algorithms, and Applications 2nd Edition
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'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego
'Some machine learning books cover only programming aspects, often relying on outdated software tools; some focus exclusively on neural networks; others, solely on theoretical foundations; and yet more books detail advanced topics for the specialist. This fully revised and expanded text provides a broad and accessible introduction to machine learning for engineering and computer science students. The presentation builds on first principles and geometric intuition, while offering real-world examples, commented implementations in Python, and computational exercises. I expect this book to become a key resource for students and researchers.' Osvaldo Simeone, Kings College London
'This book is great for getting started in machine learning. It builds up the tools of the trade from first principles, provides lots of examples, and explains one thing at a time at a steady pace. The level of detail and runnable code show what's really going when we run a learning algorithm.' David Duvenaud, University of Toronto
'This book covers various essential machine learning methods (e.g., regression, classification, clustering, dimensionality reduction, and deep learning) from a unified mathematical perspective of seeking the optimal model parameters that minimize a cost function. Every method is explained in a comprehensive, intuitive way, and mathematical understanding is aided and enhanced with many geometric illustrations and elegant Python implementations.' Kimiaki Sihrahama, Kindai University, Japan
'Books featuring machine learning are many, but those which are simple, intuitive, and yet theoretical are extraordinary 'outliers'. This book is a fantastic and easy way to launch yourself into the exciting world of machine learning, grasp its core concepts, and code them up in Python or Matlab. It was my inspiring guide in preparing my 'Machine Learning Blinks' on my BASIRA YouTube channel for both undergraduate and graduate levels.' Islem Rekik, Director of the Brain And SIgnal Research and Analysis (BASIRA) Laboratory
An intuitive approach to machine learning detailing the key concepts needed to build products and conduct research. Featuring color illustrations, real-world examples, practical coding exercises, and an online package including sample code, data sets, lecture slides, and solutions. It is ideal for graduate courses, reference, and self-study.
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This is one of the best books on ML today; the 1-star rating is only for the poor formatting of the Kindle edition, not the book's contents. The Kindle edition's equations and graphs are small to the point of illegibility. I have both the print and Kindle editions. I use the Kindle (iPad & PC) edition when I commute or travel. The iPad Kindle edition graphs are usable, but many equations are too small to see. The PC Kindle edition has very poor sizing of both equations and graphs - to the point that you need a screen magnifier to see any details. I purposely gave this book a 1-star rating in the hope that they'd update the Kindle editions to make the equations and graphs usable. I will revise the rating to 5-stars if they do. Until then, you may want to stick with the print edition only. The content imparts an intuitive, but deep understanding of the subject and is well worth it.