Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

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Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

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Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

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Mã sản phẩm:
Danh mục: Sách ngoại ngữ, Sách Tiếng Anh quyển Lẻ
Tình trạng: Còn hàng

Mô tả

Machine Learning The Art and Science of Algorithms that Make Sense of Data (sách tiếng anh)

Sách keo gáy, Bìa mềm

Thể loại:Computers - Artificial Intelligence (AI)

Năm:2012

Ngôn ngữ:english

Trang:409 / 416

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.

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