Fundamentals of Machine Learning for Predictive Data Analytics : Algorithms, Worked Examples, and Case Studies, 2/e (Hardcover)

Kelleher, John D., Mac Namee, Brian, D'Arcy, Aoife

  • 出版商: MIT
  • 出版日期: 2020-10-20
  • 定價: $1,450
  • 售價: 9.8$1,421
  • 語言: 英文
  • 頁數: 856
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 0262044692
  • ISBN-13: 9780262044691
  • 相關分類: Machine LearningAlgorithms-data-structuresData Science
  • 立即出貨 (庫存 < 4)

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商品描述

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.

商品描述(中文翻譯)

這是一本全面介紹機器學習方法在預測性數據分析中應用的第二版教材,涵蓋理論和實踐兩方面。

機器學習通常用於從大型數據集中提取模式以建立預測模型。這些模型應用於預測性數據分析應用中,包括價格預測、風險評估、預測客戶行為和文件分類。這本入門教材詳細介紹了在預測性數據分析中使用的最重要的機器學習方法,涵蓋了理論概念和實際應用。技術和數學材料配有解釋性的實例,案例研究則展示了這些模型在更廣泛的商業背景中的應用。這本第二版涵蓋了機器學習的最新發展,特別是在一個新的章節中介紹了深度學習,還新增了兩個章節,介紹了無監督學習和強化學習。

這本書易於理解,對每種方法的基本概念進行了非技術性的解釋,然後介紹了數學模型和算法。它的焦點和深度使學生能夠深入了解核心概念,為他們自己探索這個領域奠定了堅實的基礎。早期章節和後期案例研究都說明了學習預測模型的過程如何融入更廣泛的商業背景中。兩個案例研究描述了具體的數據分析項目,從制定商業問題到實施分析解決方案的每個階段。這本書可以作為入門級教材使用,也可以作為專業人士的參考書。

作者簡介

John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at Technological University Dublin. He is the coauthor of Data Science and the author of Deep Learning, both in the MIT Press Essential Knowledge series.

Brian Mac Namee is Associate Professor at the School of Computer Science at University College Dublin

Aoife D'Arcy is CEO of Krisolis, a data analytics company based in Dublin.

作者簡介(中文翻譯)

John D. Kelleher是都柏林科技大學資訊、通訊和娛樂研究所的學術領導者。他是MIT Press Essential Knowledge系列中《資料科學》的合著者,也是《深度學習》的作者。

Brian Mac Namee是都柏林大學計算機科學學院的副教授。

Aoife D'Arcy是位於都柏林的數據分析公司Krisolis的首席執行官。