Data mining : practical machine learning tools and techniques /
"Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclai...
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| Otros Autores: | , , , , |
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| Formato: | Libro |
| Lenguaje: | Inglés |
| Publicado: |
Cambridge, Mass. :
Morgan Kaufmann,
2026.
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| Edición: | Fifth Edition |
| Materias: | |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
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| 245 | 0 | 0 | |a Data mining : |b practical machine learning tools and techniques / |c Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal, James R. Foulds. |
| 250 | |a Fifth Edition | ||
| 264 | 1 | |a Cambridge, Mass. : |b Morgan Kaufmann, |c 2026. | |
| 300 | |a xl, 760 páginas : |b ilustraciones, gráficos ; |c 24 cm | ||
| 336 | |a texto |b txt |2 rdacontent | ||
| 337 | |a sin mediación |b n |2 rdamedia | ||
| 338 | |a volumen |b nc |2 rdacarrier | ||
| 505 | 0 | |a Introduction to data mining. What's it all about?. Input: concepts, instances, attributes. Output: knowledge representation. Algorithms: te basic methods. Credibility: evaluating what's been learned. Preparation: data preprocessing and exploratory data analysis. Ethics: what are the impacts of what's been learned? -- More advanced Machine Learning Schemes. Ensemble learning. Extending instance-based and linear models. Deep learning: fundamentals. Advanced deep learning methods. Beyond supervised and unsupervised learning. Probabilistic methods: fundamentals. Advanced probabilistic methods. Moving on: applications and their consequences. | |
| 520 | 3 | |a "Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches." | |
| 520 | |a "Minería de Datos: Herramientas y Técnicas Prácticas de Aprendizaje Automático, quinta edición, ofrece una base sólida en conceptos de aprendizaje automático, así como consejos prácticos para aplicar herramientas y técnicas de aprendizaje automático en situaciones reales de minería de datos. Esta esperadísima nueva edición de la obra más aclamada sobre minería de datos y aprendizaje automático le enseñará todo lo necesario sobre la preparación de entradas, la interpretación de salidas, la evaluación de resultados y los métodos algorítmicos fundamentales para una minería de datos exitosa." | ||
| 650 | 7 | |a Base de datos |2 lemb3 |9 114 | |
| 650 | 7 | |a Algoritmos |2 lemb3 |9 2773 | |
| 650 | 7 | |a Análisis de datos |2 mpirdes |9 40659 | |
| 700 | 1 | |a Witten, Ian H., |q 1947-2023 |e autor |9 108806 | |
| 700 | 1 | |a Frank, Eibe |e autor |9 108807 | |
| 700 | 1 | |a Hall, Mark A. |e autor |9 108808 | |
| 700 | 1 | |a Pal, Christopher J. |e autor |9 108809 | |
| 700 | 1 | |a Foulds, James R. |e autor |9 108810 | |
| 942 | |2 cdu |b 2026-02-25 |c BK |h 004.42=20 |i DATw5 |z NG |6 0044220_DATW5 | ||