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Mastering Machine Learning with R, Second Edition

You're reading from   Mastering Machine Learning with R, Second Edition Advanced prediction, algorithms, and learning methods with R 3.x

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Length 420 pages
Edition 2nd Edition
Languages
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (17) Chapters Close

Preface 1. A Process for Success FREE CHAPTER 2. Linear Regression - The Blocking and Tackling of Machine Learning 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques - K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks and Deep Learning 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis 11. Creating Ensembles and Multiclass Classification 12. Time Series and Causality 13. Text Mining 14. R on the Cloud 15. R Fundamentals 16. Sources

Sources

Granger, G.W.J., Newbold, P., (1974), Spurious Regressions in Econometrics, Journal of Econometrics, 1974 (2), 111-120

Hechenbichler, K., Schliep, K.P., (2004), Weighted k-Nearest-Neighbors and Ordinal Classification,  Institute for Statistics, Sonderforschungsbereich 386, Paper 399. http://epub.ub.uni-muenchen.de/

Hinton, G.E., Salakhutdinov, R.R., (2006), Reducing the Dimensionality of Data with Neural Networks, Science, August 2006, 313(5786):504-7

James, G., Witten, D., Hastie, T., Tisbshirani, R. (2013), An Introduction to Statistical Learning, 1st ed. New York: Springer

Kodra, E., (2011), Exploring Granger Causality Between Global Average Observed Time Series of Carbon Dioxide and Temperature, Theoretical and Applied Climatology, Vol. 104 (3), 325-335

Natekin, A., Knoll, A., (2013),  Gradient Boosting Machines, a Tutorial, Frontiers in Neurorobotics, 2013; 7-21. https://www.ncbi.nlm...

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