Machine Learning with R - Third Edition
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| About |
Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R. |
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| Page Count | 458 |
| Course Length | 13 hours 44 minutes |
| ISBN | 9781788295864 |
| Date Of Publication | 14 Apr 2019 |
| Understanding nearest neighbor classification |
| Example – diagnosing breast cancer with the k-NN algorithm |
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| Understanding Naive Bayes |
| Example – filtering mobile phone spam with the Naive Bayes algorithm |
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| Understanding decision trees |
| Example – identifying risky bank loans using C5.0 decision trees |
| Understanding classification rules |
| Example – identifying poisonous mushrooms with rule learners |
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| Understanding regression |
| Example – predicting medical expenses using linear regression |
| Understanding regression trees and model trees |
| Example – estimating the quality of wines with regression trees and model trees |
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| Understanding neural networks |
| Example – modeling the strength of concrete with ANNs |
| Understanding support vector machines |
| Example – performing OCR with SVMs |
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| Understanding association rules |
| Example – identifying frequently purchased groceries with association rules |
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