☆☆☆☆☆




點擊這裡下載

類似圖書 點擊查看全場最低價

---

內容簡介

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, example-based 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 well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art 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.

作者簡介

Peter Flach has more than twenty years of experience in machine learning teaching and research. He is Editor-in-Chief of Machine Learning and Program Co-Chair of the 2009 ACM Conference on Knowledge Discovery and Data Mining and the 2012 European Conference on Machine Learning and Data Mining. His research spans all aspects of machine learning, from knowledge representation and the use of logic to learn from highly structured data to the analysis and evaluation of machine learning models and methods to large-scale data mining. He is particularly known for his innovative use of Receiver Operating Characteristic (ROC) analysis for understanding and improving machine learning methods. These innovations have proved their effectiveness in a number of invited talks and tutorials and now form the backbone of this book.

精彩書評

"This textbook is clearly written and well organized. Starting from the basics, the author skillfully guides the reader through his learning process by providing useful facts and insight into the behavior of several machine learning techniques, as well as the high-level pseudocode of many key algorithms."
——Fernando Berzal, Computing Reviews

前言/序言

Machine Learning: The Art and Science of Algorithms that Make Sense of Data [平裝] 下載 mobi epub pdf txt 電子書

評分☆☆☆☆☆

這本書一般，雖然都是案例，但是寫得太過簡略，並且案例選擇實用性不大，僅僅是讓初學者對機器學習怎麼寫代碼有個瞭解。

評分☆☆☆☆☆

幫人買的，很正版的書籍，同事很喜歡

評分☆☆☆☆☆

商品包裝有待進一步加強。

評分☆☆☆☆☆

寫的很不錯的書。

評分☆☆☆☆☆

都是跟風學這個吧，作為快速入門的書籍有點用，偏實踐。想要機器學習的內涵講解，這本書是遠遠不夠的

評分☆☆☆☆☆

有人推薦這本書。買來看一下，紙質書有感覺。

評分☆☆☆☆☆

主要介紹瞭泛化的綫性迴歸方法和提升方法（包括RF、GBDT），內容不錯，不過可能由於第一次印刷，還存在很多錯誤。

評分☆☆☆☆☆

很好很好很好很好很好很好很好很好很好很好很好很好

評分☆☆☆☆☆

618囤貨中，經濟實惠。給力，期待明年

類似圖書 點擊查看全場最低價

查看全網最低價