Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / [electronic resource]
- 作者： Kelleher, John D., 1974- author.
- 其他作者：
- 其他題名：
- Algorithms, worked examples, and case studies
- 出版： Cambridge, Massachusetts : MIT Press
- 版本：Second edition.
- 主題： Machine learning. , Data mining. , Prediction theory. , Data Mining , Machine Learning , Apprentissage automatique. , Exploration de donnees (Informatique) , Theorie de la prevision. , COMPUTERS--Artificial Intelligence--General. , Data mining , Machine learning , Prediction theory
- ISBN： 9780262361101 (electronic bk.) 、 0262361108 (electronic bk.) 、 9780262364911 (electronic bk.) 、 0262364913 (electronic bk.)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Introduction to machine learning and data analytics: Machine learning for predictive data analysis -- Data to insights to decisions -- Data exploration -- Predictive data analytics: Information-based learning -- Probaility-based learning -- Error-based learning -- Deep learning -- Evaluation -- Beyond prediction: Beyond prediction: unsupervised learning -- Beyond prediction: reinforcement learning -- Case studies and conclusions: Case study: customer churn -- Case study: galaxy classification -- The art of machine learning for predictive data analytics -- Appendices: Descriptive statistics and data visualization for machine learning -- Introduction to probability for machine learning -- Differentiation techniques -- Introduction to linear algrbra. Includes bibliographical references and index. Available through EBookCentral.
- 摘要註: A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
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讀者標籤：
- 系統號： 005528232 | 機讀編目格式
館藏資訊
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.