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Machine Learning with R, the tidyverse, and mlr /
Machine Learning with R, the tidyverse, and mlr gets you started in machine learning using R Studio and the awesome mlr machine learning package. This practical guide simplifies theory and avoids needlessly complicated statistics or math. All core ML techniques are clearly explained through graphics...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Manning Publications,
2020.
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| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a Machine Learning with R, the tidyverse, and mlr / |c Rhys, Hefin. |
| 250 | |a 1st edition. | ||
| 264 | 1 | |b Manning Publications, |c 2020. | |
| 300 | |a 1 online resource (536 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 520 | |a Machine Learning with R, the tidyverse, and mlr gets you started in machine learning using R Studio and the awesome mlr machine learning package. This practical guide simplifies theory and avoids needlessly complicated statistics or math. All core ML techniques are clearly explained through graphics and easy-to-grasp examples. In each engaging chapter, you'll put a new algorithm into action to solve a quirky predictive analysis problem, including Titanic survival odds, spam email filtering, and poisoned wine investigation. | ||
| 542 | |f © 2020 Manning Publications Co. All rights reserved. |g 2020 | ||
| 550 | |a Made available through: Safari, an O'Reilly Media Company. | ||
| 588 | |a Online resource; Title from title page (viewed April 4, 2020) | ||
| 505 | 0 | |a Part 1. Introduction. Introduction to machine learning -- Tidying manipulating and plotting data with the tidyverse -- Part 2. Classification. Classifying based on similarities with k-nearest neighbors -- Classifying based on odds with logistic regression -- Classifying by maximizing separation with discriminant analysis -- Classifying with naive Bayes and support vector machines -- Classifying with decision trees -- Improving decision trees with random forests and boosting -- Part 3. Regression. Linear regression -- Nonlinear regression with generalized additive models -- Preventing overfitting with ridge regression, LASSO, and elastic net -- Regression with kNN, random forest, and XGBoost -- Part 4. Dimension reduction. Maximizing variance with principal component analysis -- Maximizing similarity with t-SNE and UMAP -- Self-organizing maps, and locally linear embedding -- Part 5. Clustering. Clustering by finding centers with k-means -- Hierarchical clustering -- Clustering based on density: DBSCAN and OPTICS -- Clustering based on distributions with mixture modeling -- Final notes and further reading. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 2 | |a Machine Learning | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 710 | 2 | |a Safari, an O'Reilly Media Company. | |
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