Dēta anaritikusu no tame no kikai gakushū nyūmon :: arugorizumu, jitsurei, kēsu sutadi /
データアナリティクスのための機械学習入門 : アルゴリズム・実例・ケーススタディ /
"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 textbo...
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| Hauptverfasser: | , , |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | Japanese English |
| Veröffentlicht: |
Tōkyō-to Shinjuku-ku :
Kindai Kagakusha,
2022.
|
| Ausgabe: | Shohan. |
| Schriftenreihe: | Sekai hyōjun MIT kyōkasho.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "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. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals"--Provided by publisher. |
| Beschreibung: | 1 online resource (xvi, 454 pages). : illustrations. |
| Bibliographie: | Includes bibliographical references (pages 438-447) and index. |
| ISBN: | 9784764972902 4764972905 |
Internformat
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| 245 | 1 | 0 | |6 880-01 |a Dēta anaritikusu no tame no kikai gakushū nyūmon : |b arugorizumu, jitsurei, kēsu sutadi / |c cho J.D. Kerahā, B. Makunamī, A. Dāshī ; yaku Miyaoka Etsuo, Shimokawa Asanao, Kurosawa Takuma = Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / J.D. Kelleher, B. Mac Namee, A. D'Arcy. |
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| 520 | |a "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. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals"--Provided by publisher. | ||
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| illustrated | Illustrated |
| indexdate | 2024-11-27T13:30:38Z |
| institution | BVB |
| isbn | 9784764972902 4764972905 |
| language | Japanese English |
| oclc_num | 1347378768 |
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| series | Sekai hyōjun MIT kyōkasho. |
| series2 | Sekai hyōjun MIT kyōkasho |
| spelling | Kelleher, John D., 1974- author. https://id.oclc.org/worldcat/entity/E39PBJkTbwcmCvMvcxQJmydxXd http://id.loc.gov/authorities/names/n2014074189 Fundamentals of machine learning for predictive data analytics. Japanese 880-01 Dēta anaritikusu no tame no kikai gakushū nyūmon : arugorizumu, jitsurei, kēsu sutadi / cho J.D. Kerahā, B. Makunamī, A. Dāshī ; yaku Miyaoka Etsuo, Shimokawa Asanao, Kurosawa Takuma = Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / J.D. Kelleher, B. Mac Namee, A. D'Arcy. Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies 880-02 Shohan. 880-03 Tōkyō-to Shinjuku-ku : Kindai Kagakusha, 2022. 1 online resource (xvi, 454 pages). : illustrations. text txt rdacontent computer c rdamedia online resource cr rdacarrier 880-04 Sekai hyōjun MIT kyōkasho Online resource; title from PDF title page (EBSCO, viewed October 17, 2022). Includes bibliographical references (pages 438-447) and index. "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. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals"--Provided by publisher. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Prediction theory. http://id.loc.gov/authorities/subjects/sh85106258 Apprentissage automatique. Exploration de données (Informatique) Théorie de la prévision. Data mining fast Machine learning fast Prediction theory fast 880-05 Kikai gakushū. jlabsh/4 Mac Namee, Brian, author. D'Arcy, Aoife, 1978- author. https://id.oclc.org/worldcat/entity/E39PCjxMytRFtfcDBxJXmJMqXq http://id.loc.gov/authorities/names/n2014074193 880-06 Miyaoka, Etsuo, translator. 880-07 Sekai hyōjun MIT kyōkasho. FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3365775 Volltext 245-01/$1 データアナリティクスのための機械学習入門 : アルゴリズム・実例・ケーススタディ / 著J.D. ケラハー, B. マクナミー, A. ダーシー ; 訳宮岡悦良, 下川朝有, 黒沢匠雅 = Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / J.D. Kelleher, B. Mac Namee, A. D'Arcy. 250-02/$1 初版. 264-03/$1 東京都新宿区 : 近代科学社, 2022. 490-04/$1 世界標準MIT教科書 520-00/$1 "本書は機械学習を実際のビジネスシーンに適用してデータ分析を行うための実践書である。機械学習そのものの解説というよりは、データ分析に不可欠な機械学習の手法を駆使してビジネスを予測的に改善する手法を解説していく。具体的な適用事例を用いて説明がなされるため、読者は目的やケースに合った手法（アルゴリズム）や実際の適用方法などを効率的に身に付けることができる。原著はMITで使われている教科書であり、講義の目的に応じて章を選択可能。ビジネスで使えるデータ分析手法を最短で習得したい読者に役立つ一冊である。"-- Provided by publisher. 650-05/$1 機械学習. jlabsh/4 700-06/$1 宮岡悦良, translator. 830-07/$1 世界標準MIT教科書. |
| spellingShingle | Kelleher, John D., 1974- Mac Namee, Brian D'Arcy, Aoife, 1978- Dēta anaritikusu no tame no kikai gakushū nyūmon : arugorizumu, jitsurei, kēsu sutadi / Sekai hyōjun MIT kyōkasho. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Prediction theory. http://id.loc.gov/authorities/subjects/sh85106258 Apprentissage automatique. Exploration de données (Informatique) Théorie de la prévision. Data mining fast Machine learning fast Prediction theory fast 880-05 Kikai gakushū. jlabsh/4 |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh97002073 http://id.loc.gov/authorities/subjects/sh85106258 |
| title | Dēta anaritikusu no tame no kikai gakushū nyūmon : arugorizumu, jitsurei, kēsu sutadi / |
| title_alt | Fundamentals of machine learning for predictive data analytics. Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies |
| title_auth | Dēta anaritikusu no tame no kikai gakushū nyūmon : arugorizumu, jitsurei, kēsu sutadi / |
| title_exact_search | Dēta anaritikusu no tame no kikai gakushū nyūmon : arugorizumu, jitsurei, kēsu sutadi / |
| title_full | Dēta anaritikusu no tame no kikai gakushū nyūmon : arugorizumu, jitsurei, kēsu sutadi / cho J.D. Kerahā, B. Makunamī, A. Dāshī ; yaku Miyaoka Etsuo, Shimokawa Asanao, Kurosawa Takuma = Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / J.D. Kelleher, B. Mac Namee, A. D'Arcy. |
| title_fullStr | Dēta anaritikusu no tame no kikai gakushū nyūmon : arugorizumu, jitsurei, kēsu sutadi / cho J.D. Kerahā, B. Makunamī, A. Dāshī ; yaku Miyaoka Etsuo, Shimokawa Asanao, Kurosawa Takuma = Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / J.D. Kelleher, B. Mac Namee, A. D'Arcy. |
| title_full_unstemmed | Dēta anaritikusu no tame no kikai gakushū nyūmon : arugorizumu, jitsurei, kēsu sutadi / cho J.D. Kerahā, B. Makunamī, A. Dāshī ; yaku Miyaoka Etsuo, Shimokawa Asanao, Kurosawa Takuma = Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / J.D. Kelleher, B. Mac Namee, A. D'Arcy. |
| title_short | Dēta anaritikusu no tame no kikai gakushū nyūmon : |
| title_sort | deta anaritikusu no tame no kikai gakushu nyumon arugorizumu jitsurei kesu sutadi |
| title_sub | arugorizumu, jitsurei, kēsu sutadi / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Prediction theory. http://id.loc.gov/authorities/subjects/sh85106258 Apprentissage automatique. Exploration de données (Informatique) Théorie de la prévision. Data mining fast Machine learning fast Prediction theory fast 880-05 Kikai gakushū. jlabsh/4 |
| topic_facet | Machine learning. Data mining. Prediction theory. Apprentissage automatique. Exploration de données (Informatique) Théorie de la prévision. Data mining Machine learning Prediction theory Kikai gakushū. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3365775 |
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